We have assembled a comprehensive and publicly accessible U.S. Geological Survey (USGS) streamflow measurement data set, called HYDRoSWOT, from a USGS National Water Information System archive of acoustic Doppler current profiler river discharge measurements collected from a wide range of rivers throughout the United States. The data set provides a wealth of information on the range of hydraulic characteristics of river cross sections in the United States. Preliminary exploration of the data set, filtered for quality control, indicates that rivers tend toward consistent and predictable forms as discharge increases. The ratio of maximum-to-mean depth is highly predictable and is remarkably consistent across all river sizes and discharges. Distributions of hydraulic characteristics provide a large-scale perspective on the general hydraulic characteristics of rivers. The data set affords the opportunity to analyze hydraulic relations for individual rivers as a function of stage, geomorphic setting, and energy environments and, combined with additional information contained in this data set, might yield predictive relations that could help constrain and parameterize river hydraulic models.
Stressors such as antibiotics, herbicides, and pollutants are becoming increasingly common in the environment. The effects of stressors on populations are typically studied in homogeneous, nonspatial settings. However, most populations in nature are spatially distributed over environmentally heterogeneous landscapes with spatially restricted dispersal. Little is known about the effects of stressors in these more realistic settings. Here, we combine laboratory experiments with novel mathematical theory to rigorously investigate how a stressor’s physiological effect and spatial distribution interact with dispersal to influence population dynamics. We prove mathematically that if a stressor increases the death rate and/or simultaneously decreases the population growth rate and yield, a homogeneous distribution of the stressor leads to a lower total population size than if the same amount of the stressor was heterogeneously distributed. We experimentally test this prediction on spatially distributed populations of budding yeast (Saccharomyces cerevisiae). We find that the antibiotic cycloheximide increases the yeast death rate but reduces the growth rate and yield. Consistent with our mathematical predictions, we observe that a homogeneous spatial distribution of cycloheximide minimizes the total equilibrium size of experimental metapopulations, with the magnitude of the effect depending predictably on the dispersal rate and the geographic pattern of antibiotic heterogeneity. Our study has implications for assessing the population risk posed by pollutants, antibiotics, and global change and for the rational design of strategies for employing toxins to control pathogens and pests.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/709293","usgsCitation":"Zhang, B., DeAngelis, D., Ni, W., Wang, Y., Zhai, L., Kula, A., Xu, S., and Van Dyken, J.D., 2020, Effect of stressors on the carrying capacity of spatially distributed metapopulations: The American Naturalist, v. 196, no. 2, p. E46-E60, https://doi.org/10.1086/709293.","productDescription":"15 p.","startPage":"E46","endPage":"E60","ipdsId":"IP-096375","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437072,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KHRJKW","text":"USGS data release","linkHelpText":"Scaling antibiotic efficacy from cells to metapopulations"},{"id":373943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"196","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Bo","contributorId":146526,"corporation":false,"usgs":false,"family":"Zhang","given":"Bo","email":"","affiliations":[{"id":16714,"text":"Dept. of Biology, University of Miami","active":true,"usgs":false}],"preferred":false,"id":786864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":786865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ni, Wei-Ming","contributorId":146528,"corporation":false,"usgs":false,"family":"Ni","given":"Wei-Ming","email":"","affiliations":[{"id":16716,"text":"University of Minnesota : East China Normal University","active":true,"usgs":false}],"preferred":false,"id":786866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Yuanshi","contributorId":207814,"corporation":false,"usgs":false,"family":"Wang","given":"Yuanshi","email":"","affiliations":[{"id":37637,"text":"School of Mathematics and Computational Science Sun Yat-sen University","active":true,"usgs":false}],"preferred":false,"id":786867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhai, Lu","contributorId":202653,"corporation":false,"usgs":false,"family":"Zhai","given":"Lu","email":"","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":786868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kula, Alex","contributorId":194890,"corporation":false,"usgs":false,"family":"Kula","given":"Alex","email":"","affiliations":[],"preferred":false,"id":786869,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xu, Shuang","contributorId":224047,"corporation":false,"usgs":false,"family":"Xu","given":"Shuang","email":"","affiliations":[{"id":13532,"text":"Department of Biology, University of Miami","active":true,"usgs":false}],"preferred":false,"id":786870,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Dyken, J. David","contributorId":194913,"corporation":false,"usgs":false,"family":"Van Dyken","given":"J.","email":"","middleInitial":"David","affiliations":[],"preferred":false,"id":786871,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208937,"text":"70208937 - 2020 - Climate dipoles as continental drivers of plant and animal populations","interactions":[],"lastModifiedDate":"2020-05-05T17:07:58.968548","indexId":"70208937","displayToPublicDate":"2020-03-05T06:56:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Climate dipoles as continental drivers of plant and animal populations","docAbstract":"Ecological processes, such as migration and phenology, are strongly influenced by climate variability. Studying these processes often relies on associating observations of animals and plants with climate variability indices, such as the El Niño–Southern Oscillation. A characteristic of climate indices is the simultaneous emergence of opposite extremes of temperature and precipitation across continental scales, known as climate dipoles. The role of climate dipoles in shaping ecological and evolutionary processes has been largely overlooked. We review emerging evidence that climate dipoles can entrain species dynamics, and offer a framework for identifying ecological dipoles using broad-scale biological data. Given future changes in climatic and atmospheric processes, climate and ecological dipoles will likely shift in their intensity, distribution, and timing.","language":"English","publisher":"Elsevier","doi":"10.1016/j.tree.2020.01.010","usgsCitation":"Zuckerberg, B., Strong, C., LaMontagne, J., St. George, S., Betancourt, J.L., and Koenig, W.D., 2020, Climate dipoles as continental drivers of plant and animal populations: Trends in Ecology and Evolution, v. 35, no. 5, p. 440-453, https://doi.org/10.1016/j.tree.2020.01.010.","productDescription":"14 p.","startPage":"440","endPage":"453","ipdsId":"IP-116563","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":372989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zuckerberg, Benjamin","contributorId":200298,"corporation":false,"usgs":false,"family":"Zuckerberg","given":"Benjamin","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":784102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strong, Courtenay","contributorId":195262,"corporation":false,"usgs":false,"family":"Strong","given":"Courtenay","email":"","affiliations":[],"preferred":false,"id":784103,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaMontagne, Jalene M.","contributorId":223096,"corporation":false,"usgs":false,"family":"LaMontagne","given":"Jalene","middleInitial":"M.","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":784104,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"St. George, Scott","contributorId":218756,"corporation":false,"usgs":false,"family":"St. George","given":"Scott","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":784105,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":784106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koenig, Walter D.","contributorId":46255,"corporation":false,"usgs":false,"family":"Koenig","given":"Walter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":784107,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217011,"text":"70217011 - 2020 - Causal effect of impervious cover on annual flood magnitude for the United States","interactions":[],"lastModifiedDate":"2020-12-28T12:49:18.302259","indexId":"70217011","displayToPublicDate":"2020-03-05T06:30:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Causal effect of impervious cover on annual flood magnitude for the United States","docAbstract":"Despite consensus that impervious surfaces increase flooding, the magnitude of the increase remains uncertain. This uncertainty largely stems from the challenge of isolating the effect of changes in impervious cover separate from other factors that also affect flooding. To control for these factors, prior study designs rely on either temporal or spatial variation in impervious cover. We leverage both temporal and spatial variation in a panel data regression design to isolate the effect of impervious cover on floods. With 39 years of data from 280 U.S. streamgages, we estimate that a one percentage point increase in impervious basin cover causes a 3.3% increase in annual flood magnitude (95%CI: 1.9%, 4.7%) on average. Using 2,109 streamgages, some of which have upstream regulation and/or overlapping basins, we estimate a larger effect: 4.6% (CI: 3.5%, 5.6%). The approach introduced here can be extended to estimate the causal effects of other drivers of hydrologic change.
We evaluated the status of a population of Mojave Desert Tortoises (Gopherus agassizii), a threatened species, in the El Paso Mountains of the northwestern Mojave Desert in California, USA. The study area lies north of and adjacent to a designated critical habitat unit for the species, is adjacent to a state park, and is a short distance from the Desert Tortoise Research Natural Area. We randomly sampled 373 1-ha plots from a 239.1-km2 area in the mountain range to determine demographic attributes of the population, vegetation associations, predator presence, and human uses. Live and dead G. agassizii and sign (burrows, scats, tracks) occurred on 35.7% of plots. Densities of adults were higher than in adjacent critical habitat, and threats (traumatic injuries, infectious and other diseases) were similar to those reported elsewhere in the geographic range. Signs of human use were evident on 98.4% of plots. We used a multimodel approach to determine distribution of G. agassizii in relation to vegetation, anthropogenic, and predator variables. Vegetation, predators, trash, mining activity, and vehicles were important factors affecting the distribution and intensity of tortoise sign. We concluded that this population is in a downward trend, like other populations in the western Mojave Desert. The high death rate of adults, low population density, high human visitor use, and ongoing decline in the adjacent critical habitat unit indicate that a viable population is unlikely to persist in the study area. The future for the population found in the El Paso Mountains might depend on survival in the adjacent roadless El Paso Mountains Wilderness Area.
","language":"English","publisher":"The Herpetologists' League, Inc","doi":"10.1655/Herpetologica-D-18-00033","usgsCitation":"Berry, K.H., Yee, J.L., Lyren, L.L., and Mack, J., 2020, An uncertain future for a population of desert tortoises experiencing human impacts: Herpetologica, v. 76, no. 1, p. 1-11, https://doi.org/10.1655/Herpetologica-D-18-00033.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-016876","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457504,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1655/herpetologica-d-18-00033","text":"Publisher Index Page"},{"id":372962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"El Paso Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.14971923828124,\n 35.29607300397548\n ],\n [\n -117.60177612304688,\n 35.29607300397548\n ],\n [\n -117.60177612304688,\n 35.66399091134812\n ],\n [\n -118.14971923828124,\n 35.66399091134812\n ],\n [\n -118.14971923828124,\n 35.29607300397548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyren, Lisa L.","contributorId":166968,"corporation":false,"usgs":false,"family":"Lyren","given":"Lisa","email":"","middleInitial":"L.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":783500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mack, Jeremy S 0000-0002-3394-8493","orcid":"https://orcid.org/0000-0002-3394-8493","contributorId":206166,"corporation":false,"usgs":false,"family":"Mack","given":"Jeremy S","affiliations":[{"id":37269,"text":"Crater Lake National Park (formerly USGS - WERC)","active":true,"usgs":false}],"preferred":false,"id":783501,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208487,"text":"sir20205012 - 2020 - Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17","interactions":[],"lastModifiedDate":"2022-04-25T21:42:26.20684","indexId":"sir20205012","displayToPublicDate":"2020-03-04T14:44:16","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5012","displayTitle":"Estimates of Water Use Associated with Continuous Oil and Gas Development in the Williston Basin, North Dakota and Montana, 2007–17","title":"Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17","docAbstract":"This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of continuous oil and gas resources in the Williston Basin was estimated in North Dakota and Montana from 2007 to 2017. Water-use data were aggregated by county and year, which were the sampling units used in this analysis. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fit for the direct, indirect, and ancillary water-use categories for each State. A 95-percent confidence interval for each parameter estimate from the linear regression models was computed as a measure of uncertainty. Additional information on uncertainty can be gained from modeling other distribution parameters, so quantile regression models of the 5th, 50th, and 95th percentiles also were fit. To assess uncertainty in the estimates from the regression models of direct, indirect, and ancillary water use, leave-one-out cross-validation was used. Model performance was evaluated with three goodness-of-fit metrics used to compare the estimates and observations of water use.
Mean annual direct and indirect water use for development of continuous oil and gas resources in North Dakota was estimated at 4,512 million gallons (Mgal) per year (Mgal/yr), with a 95-percent confidence interval of 4,021–5,152 Mgal/yr, and in Montana was estimated at 196 Mgal/yr, with a 95-percent confidence interval of 189–203 Mgal/yr. Ancillary water use (for domestic and public supply) had an estimated annual mean of 2,753 Mgal/yr in North Dakota and 396 Mgal/yr in Montana. The coefficient from the linear regression model of direct water use was 3.86 Mgal per well and hydraulic fracturing water use was 3.70 Mgal per well for North Dakota. The mean estimate of direct water use had a 95-percent confidence interval of 3.48–4.23 Mgal per well. For North Dakota, the coefficient from the linear regression model of indirect water use was 0.453 Mgal per well, with a 95-percent confidence interval of 0.415–0.492 Mgal per well. Direct and indirect water use had a mean estimate of about 4.31 Mgal per well in North Dakota. The mean estimate of ancillary water use (for domestic and public supply) in North Dakota was 2.03 Mgal per well, with a 95-percent confidence interval of 1.76–2.31 Mgal per well. For Montana, the linear regression model of hydraulic fracturing water use had a mean estimate of 2.04 Mgal per well. The 95-percent confidence interval for the mean estimate was 1.80–2.28 Mgal per well. Direct and indirect water use in Montana had a mean estimate of 2.49 Mgal per well. The mean estimate of ancillary water use (for domestic and public supply) in Montana was 2.43 Mgal per well, with a 95-percent confidence interval of 1.76–3.11 Mgal per well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205012","collaboration":"Water Availability and Use Science Program","usgsCitation":"McShane, R.R., Barnhart, T.B., Valder, J.F., Haines, S.S., Macek-Rowland, K.M., Carter, J.M., Delzer, G.C., and Thamke, J.N., 2020, Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17: U.S. Geological Survey Scientific Investigations Report 2020–5012, 26 p., https://doi.org/10.3133/sir20205012","productDescription":"Report: vii, 26 p.; 2 Appendixes; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112448","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109737.htm"},{"id":372867,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 2","linkHelpText":"– Water-Use Estimates and Coefficients"},{"id":372866,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 1","linkHelpText":"– R Scripts"},{"id":372864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5012/coverthb2.jpg"},{"id":372868,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to Estimate Water Use Associated with Continuous Oil and Gas Development, Williston Basin, United States, 1980-2017 (ver. 2.0, September 2019)"},{"id":372865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5012"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8333,\n 44.8333\n ],\n [\n -99,\n 44.8333\n ],\n [\n -99,\n 49\n ],\n [\n -106.8333,\n 49\n ],\n [\n -106.8333,\n 44.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.
The fire regime is a central framing concept in wildfire science and ecology and describes how a range of wildfire characteristics vary geographically over time. Understanding and mapping fire regimes is important for guiding appropriate management and risk reduction strategies and for informing research on drivers of global change and altered fire patterns. Most efforts to spatially delineate fire regimes have been conducted by identifying natural groupings of fire parameters based on available historical fire data. This can result in classes with similar fire characteristics but wide differences in ecosystem types. We took a different approach and defined fire regime ecoregions for California to better align with ecosystem types, without using fire as part of the definition. We used an unsupervised classification algorithm to segregate the state into spatial clusters based on distinctive biophysical and anthropogenic attributes that drive fire regimes – and then used historical fire data to evaluate the ecoregions. The fire regime ecoregion map corresponded well with the major land cover types of the state and provided clear separation of historical patterns in fire frequency and size, with lower variability in fire severity. This methodology could be used for mapping fire regimes in other regions with limited historical fire data or forecasting future fire regimes based on expected changes in biophysical characteristics.
Sediment is one of the leading pollutants in rivers and streams across the United States (US) and the world. Between 1992 and 2012, concentrations of annual mean suspended sediment decreased at over half of the 137 stream sites assessed across the contiguous US. Increases occurred at less than 25 % of the sites, and the direction of change was uncertain at the remaining 25 %. Sediment trends were characterized using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model, and decreases in sediment ranged from −95 % to −8.5 % of the 1992 concentration. To explore potential drivers of these changes, the sediment trends were (1) parsed into two broad contributors of change, changes in land management versus changes in the streamflow regime, and (2) grouped by land use of the watershed and correlated to concurrent changes in land use or land cover (land use/cover), hydrology and climate variables and static/long-term watershed characteristics. At 83 % of the sites, changes in land management (captured by changes in the concentration–streamflow relationship over time; C–Q relationship) contributed more to the change in the sediment trend than changes in the streamflow regime alone (i.e., any systematic change in the magnitude, frequency or timing of flows). However, at >50 % of the sites, changes in the streamflow regime contributed at least a 5 % change in sediment, and at 11 sites changes in the streamflow regime contributed over half the change in sediment, indicating that at many sites changes in streamflow were not the main driver of changes in sediment but were often an important supporting factor. Correlations between sediment trends and concurrent changes in land use/cover, hydrology and climate were often stronger at sites draining watersheds with more homogenous, human-related land uses (i.e., agricultural and urban lands) compared to mixed-use or undeveloped lands. At many sites, decreases in sediment occurred despite small-to-moderate increases in the amount of urban or agricultural land in the watershed, suggesting conservation efforts and best-management practices (BMPs) used to reduce sediment runoff to streams may be successful, up to a point, as lands are converted to urban and agricultural uses.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-24-991-2020","usgsCitation":"Murphy, J.C., 2020, Changing suspended sediment in United States rivers and streams: Linking sediment trends to changes in land use/cover, hydrology and climate: Hydrology and Earth System Sciences, v. 24, p. 991-1010, https://doi.org/10.5194/hess-24-991-2020.","productDescription":"20 p.","startPage":"991","endPage":"1010","ipdsId":"IP-105905","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457510,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-991-2020","text":"Publisher Index Page"},{"id":372922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"24","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":783837,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208666,"text":"70208666 - 2020 - Climate change: Flowering time may be shifting in surprising ways","interactions":[],"lastModifiedDate":"2020-03-05T14:10:34","indexId":"70208666","displayToPublicDate":"2020-03-03T14:09:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1352,"text":"Current Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change: Flowering time may be shifting in surprising ways","docAbstract":"Climate change is known to affect regional weather patterns and phenology; however, we lack under-standing of how climate drives phenological change across local spatial gradients. This spatial variation is critical for determining whether subpopulations and metacommunities are changing in unison or diverging in phenology. Divergent responses could reduce synchrony both within species (disrupting gene flow among subpopulations) and among species (disrupting interspecific interactions in communities). We also lack understanding of phenological change in environments where life history events are frequently aseasonal, such as the tropical, arid,and semi-arid ecosystems that cover vast areas.Using a 33-year-long dataset spanning a 1,267-m semi-arid elevational gradient in the southwestern United States, we test whether flowering phenology diverged among subpopulations within species and among five communities comprising 590 species. Applying circular statistics to test for changes in year-round flowering, we show flowering has become earlier for all communities except at the highest elevations. However, flowering times shifted at different rates across elevations likely because of elevation-specific changes in temperature and precipitation, indicating diverging phenologies of neighboring communities. Subpopulations of individual species also diverged at mid-elevation but converged in phenology at high elevation. These changes in flowering phenology among communities and subpopulations are undetectable when data are pooled across the gradient. Furthermore, we show that nonlinear changes in flowering times over the 33-year record are obscured by traditional calculations of long-term trends. These findings reveal greater spatiotemporal complexity in phenological responses than previously recognized and indicate climate is driving phenological reshuffling acrosslocal spatial gradients.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2019.12.009","usgsCitation":"Prevey, J.S., 2020, Climate change: Flowering time may be shifting in surprising ways: Current Biology, v. 30, no. 3, p. R112-R114, https://doi.org/10.1016/j.cub.2019.12.009.","productDescription":"3 p.","startPage":"R112","endPage":"R114","ipdsId":"IP-114283","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":457512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cub.2019.12.009","text":"Publisher Index Page"},{"id":372955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prevey, Janet S. 0000-0003-2879-6453","orcid":"https://orcid.org/0000-0003-2879-6453","contributorId":222702,"corporation":false,"usgs":true,"family":"Prevey","given":"Janet","email":"","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":782946,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70209057,"text":"70209057 - 2020 - Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database","interactions":[],"lastModifiedDate":"2020-03-12T12:52:37","indexId":"70209057","displayToPublicDate":"2020-03-03T12:46:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database","docAbstract":"The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001-2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001-2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this time. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4,778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2020.02.019","usgsCitation":"Homer, C.G., Dewitz, J., Jin, S., Xian, G.Z., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R.F., and Riitters, K.H., 2020, Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184-199, https://doi.org/10.1016/j.isprsjprs.2020.02.019.","productDescription":"16 p.","startPage":"184","endPage":"199","ipdsId":"IP-113469","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457514,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2020.02.019","text":"Publisher Index Page"},{"id":373197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"162","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Costello, Catherine 0000-0001-7158-2675","orcid":"https://orcid.org/0000-0001-7158-2675","contributorId":223238,"corporation":false,"usgs":true,"family":"Costello","given":"Catherine","email":"","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":784655,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":784660,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":784657,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Funk, Michelle 0000-0002-2772-2041","orcid":"https://orcid.org/0000-0002-2772-2041","contributorId":223239,"corporation":false,"usgs":true,"family":"Funk","given":"Michelle","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":784661,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wickham, James","contributorId":140259,"corporation":false,"usgs":false,"family":"Wickham","given":"James","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":784662,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stehman, Steven","contributorId":223240,"corporation":false,"usgs":false,"family":"Stehman","given":"Steven","email":"","affiliations":[{"id":40690,"text":"SUNY","active":true,"usgs":false}],"preferred":false,"id":784663,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Riitters, Kurt H. 0000-0003-3901-4453","orcid":"https://orcid.org/0000-0003-3901-4453","contributorId":139788,"corporation":false,"usgs":false,"family":"Riitters","given":"Kurt","email":"","middleInitial":"H.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":784665,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70210746,"text":"70210746 - 2020 - Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams","interactions":[],"lastModifiedDate":"2020-06-23T14:52:36.144242","indexId":"70210746","displayToPublicDate":"2020-03-03T09:48:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams","docAbstract":"Sediment contamination of freshwater streams in urban areas is a recognized and growing concern. As a part of a comprehensive regional stream‐quality assessment, stream‐bed sediment was sampled from streams spanning a gradient of urban intensity in the Piedmont ecoregion of the southeastern United States. We evaluated relations between a broad suite of sediment contaminants (metals, current‐use pesticides, organochlorine pesticides, polychlorinated biphenyls, brominated diphenyl ethers, and polycyclic aromatic hydrocarbons), ambient sediment toxicity, and macroinvertebrate communities from 76 sites. Sediment toxicity was evaluated by conducting whole‐sediment laboratory toxicity testing with the amphipod Hyalella azteca (for 28 d) and the midge Chironomus dilutus (for 10 d). Approximately one‐third of the sediment samples were identified as toxic for at least one test species endpoint, although concentrations of contaminants infrequently exceeded toxicity benchmarks. Ratios of contaminant concentrations relative to their benchmarks, both individually and as summed benchmark quotients, were explored on a carbon‐normalized and a dry‐weight basis. Invertebrate taxa measures from ecological surveys tended to decline with increasing urbanization and with sediment contamination. Toxicity test endpoints were more strongly related to sediment contamination than invertebrate community measures were. Sediment chemistry and sediment toxicity provided moderate and weak, respectively, explanatory power for the similarity/dissimilarity of invertebrate communities. The results indicate that current single‐chemical sediment benchmarks may underestimate the effects from mixtures of sediment contaminants experienced by lotic invertebrates.
All five species of sea turtle that occur in Florida are in danger of extinction. Many of the reasons these turtles are declining are a result of people’s actions on beaches and in shallow waters. Environmental education is needed to increase awareness and appreciation for sea turtles, and to teach about the potential harmful impacts human behaviors can have on these animals. This document describes topics that are frequently misunderstood and discusses common human actions that are harmful to sea turtles, providing insight on which topics could be addressed during environmental education efforts.
","language":"English","publisher":"UFIFAS Extension","doi":"","collaboration":"","usgsCitation":"Swindall, J.E., Ober, H.K., Lamont, M., and Carthy, R.R., 2020, Sea turtle conservation: Priorities for environmental education efforts: EDIS, v. 2, no. 4, UW46500, 4 p., https://doi.org/.","productDescription":"UW46500, 4 p.","ipdsId":"IP-116612","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":373861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373768,"type":{"id":15,"text":"Index Page"},"url":"https://journals.flvc.org/edis/article/view/117285"}],"country":"United States","state":"Florida","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-82.821585,27.964443],[-82.829801,27.968469],[-82.823063,28.044758],[-82.836326,28.073193],[-82.830525,28.085293],[-82.826125,28.083793],[-82.813435,28.03716],[-82.815168,28.012547],[-82.821755,28.002494],[-82.815168,27.973721],[-82.821585,27.964443]]],[[[-81.582923,24.658732],[-81.562917,24.692912],[-81.535323,24.67954],[-81.51898,24.687818],[-81.5124,24.703737],[-81.476642,24.711244],[-81.469275,24.704286],[-81.459043,24.707355],[-81.451881,24.714518],[-81.456588,24.740097],[-81.451267,24.747464],[-81.432032,24.722908],[-81.421595,24.737641],[-81.430599,24.747259],[-81.425483,24.752989],[-81.392947,24.743371],[-81.38558,24.726182],[-81.36041,24.708788],[-81.319282,24.701238],[-81.309664,24.665017],[-81.298028,24.656774],[-81.332831,24.639528],[-81.395096,24.621062],[-81.401946,24.623564],[-81.403319,24.640294],[-81.414187,24.647167],[-81.448623,24.640172],[-81.470411,24.641985],[-81.480951,24.645121],[-81.47641,24.653197],[-81.480504,24.659757],[-81.49858,24.66498],[-81.505585,24.654609],[-81.511165,24.625135],[-81.54645,24.614895],[-81.602998,24.586444],[-81.664209,24.573143],[-81.685278,24.558739],[-81.81289,24.546468],[-81.814446,24.56358],[-81.811386,24.56975],[-81.800676,24.570989],[-81.794057,24.586],[-81.739241,24.589973],[-81.730473,24.58196],[-81.705364,24.597647],[-81.687017,24.592534],[-81.655735,24.616295],[-81.637087,24.621408],[-81.614829,24.642764],[-81.614529,24.650584],[-81.582923,24.658732]]],[[[-82.15068,24.576331],[-82.143075,24.593395],[-82.125268,24.597426],[-82.104187,24.588256],[-82.099417,24.572522],[-82.116787,24.549144],[-82.159439,24.548212],[-82.165206,24.552159],[-82.164426,24.563375],[-82.15068,24.576331]]],[[[-81.249799,24.673357],[-81.243232,24.673998],[-81.244761,24.669202],[-81.281778,24.65375],[-81.260006,24.674848],[-81.249799,24.673357]]],[[[-80.909954,24.781154],[-80.906288,24.769867],[-80.912042,24.76505],[-80.938543,24.767535],[-81.015933,24.719881],[-81.023794,24.716901],[-81.032447,24.727323],[-81.064554,24.715453],[-81.075855,24.704266],[-81.078439,24.692382],[-81.108041,24.688592],[-81.125371,24.708291],[-81.066816,24.723926],[-81.041797,24.742965],[-81.016918,24.734676],[-80.960129,24.764226],[-80.909954,24.781154]]],[[[-81.317673,24.75729],[-81.305468,24.756612],[-81.290801,24.736862],[-81.288259,24.720881],[-81.302984,24.714199],[-81.310744,24.727068],[-81.326844,24.728375],[-81.357417,24.756834],[-81.342695,24.75625],[-81.324637,24.76721],[-81.317673,24.75729]]],[[[-80.89054,24.791678],[-80.884572,24.791561],[-80.906874,24.783744],[-80.89054,24.791678]]],[[[-80.788263,24.824218],[-80.796053,24.81194],[-80.822342,24.812629],[-80.850338,24.8026],[-80.79278,24.843918],[-80.780564,24.84052],[-80.788263,24.824218]]],[[[-80.729275,24.865361],[-80.719977,24.864644],[-80.691762,24.885759],[-80.690354,24.881539],[-80.71185,24.863323],[-80.766966,24.836158],[-80.729275,24.865361]]],[[[-84.777208,29.707398],[-84.729836,29.738881],[-84.696726,29.76993],[-84.694939,29.761844],[-84.713747,29.74139],[-84.776954,29.692191],[-84.884632,29.652248],[-84.957779,29.612635],[-85.051033,29.586928],[-85.097082,29.625215],[-85.023501,29.597073],[-85.017205,29.604379],[-84.968314,29.617238],[-84.920333,29.648638],[-84.813352,29.687028],[-84.777208,29.707398]]],[[[-85.156415,29.679628],[-85.114268,29.688658],[-85.093902,29.684838],[-85.077237,29.670862],[-85.097218,29.633004],[-85.124913,29.628433],[-85.18453,29.663987],[-85.222546,29.678039],[-85.184776,29.68271],[-85.156415,29.679628]]],[[[-82.255777,26.703437],[-82.255159,26.70816],[-82.246535,26.706435],[-82.24251,26.694361],[-82.246535,26.683437],[-82.218342,26.626407],[-82.214337,26.602944],[-82.177541,26.502328],[-82.166042,26.489679],[-82.149368,26.477605],[-82.120046,26.473581],[-82.088423,26.455182],[-82.076924,26.466106],[-82.062551,26.470131],[-82.038403,26.456907],[-82.013913,26.452058],[-82.063114,26.425459],[-82.082915,26.422059],[-82.126671,26.436279],[-82.177017,26.471558],[-82.186441,26.489221],[-82.205523,26.566536],[-82.222131,26.590402],[-82.238872,26.636433],[-82.268007,26.682791],[-82.264351,26.698496],[-82.255777,26.703437]]],[[[-80.250581,25.34193],[-80.351399,25.190615],[-80.349855,25.168825],[-80.377084,25.130487],[-80.399767,25.108536],[-80.428318,25.095547],[-80.443375,25.076084],[-80.47387,25.060253],[-80.493881,25.038502],[-80.48912,25.031301],[-80.494781,25.023019],[-80.537995,24.990244],[-80.565831,24.958155],[-80.611693,24.93842],[-80.635571,24.913003],[-80.659395,24.897433],[-80.660198,24.90498],[-80.641306,24.914311],[-80.623866,24.931236],[-80.621658,24.944265],[-80.581131,24.964738],[-80.570813,24.962215],[-80.558785,24.971505],[-80.54411,24.999916],[-80.545971,25.01477],[-80.524498,25.016945],[-80.509136,25.028317],[-80.495569,25.047497],[-80.460652,25.078904],[-80.465496,25.086609],[-80.494715,25.102269],[-80.484188,25.10943],[-80.47748,25.107407],[-80.476174,25.099454],[-80.450399,25.088751],[-80.433575,25.106317],[-80.446473,25.151287],[-80.41326,25.137053],[-80.395467,25.150694],[-80.387164,25.170859],[-80.391909,25.19221],[-80.369965,25.206444],[-80.3498,25.210595],[-80.337345,25.231353],[-80.336159,25.261601],[-80.368186,25.282359],[-80.339421,25.290069],[-80.328746,25.28651],[-80.292567,25.314385],[-80.275961,25.344039],[-80.256982,25.361239],[-80.246307,25.398603],[-80.21428,25.416988],[-80.192336,25.473331],[-80.188778,25.50773],[-80.174544,25.518406],[-80.173951,25.482821],[-80.184033,25.468587],[-80.204198,25.412244],[-80.221991,25.397417],[-80.240376,25.347005],[-80.250581,25.34193]]],[[[-83.309455,30.634417],[-82.214839,30.568591],[-82.231916,30.55627],[-82.23582,30.537187],[-82.226933,30.510281],[-82.201416,30.485164],[-82.210291,30.42459],[-82.19294,30.378779],[-82.165192,30.358035],[-82.104834,30.368319],[-82.094687,30.360781],[-82.068533,30.359184],[-82.050069,30.362338],[-82.036825,30.377884],[-82.04199,30.403266],[-82.034005,30.422357],[-82.037209,30.434518],[-82.017779,30.475081],[-82.018361,30.531184],[-82.005477,30.563495],[-82.015708,30.601704],[-82.026941,30.606153],[-82.028499,30.621829],[-82.049507,30.655548],[-82.050432,30.676266],[-82.036426,30.706585],[-82.043795,30.729641],[-82.039634,30.747727],[-82.01266,30.761289],[-82.024035,30.783156],[-82.017051,30.791657],[-82.007865,30.792937],[-81.981273,30.776767],[-81.973856,30.778487],[-81.962534,30.796526],[-81.962175,30.818001],[-81.949787,30.827493],[-81.910926,30.815889],[-81.89572,30.821098],[-81.868608,30.792754],[-81.852626,30.794439],[-81.842058,30.78712],[-81.808529,30.790014],[-81.792769,30.784432],[-81.782653,30.769937],[-81.763372,30.77382],[-81.719927,30.744634],[-81.694778,30.748414],[-81.688925,30.741434],[-81.672824,30.738935],[-81.664598,30.746599],[-81.652123,30.742435],[-81.65177,30.732284],[-81.646137,30.727591],[-81.625098,30.733017],[-81.617663,30.722046],[-81.609495,30.720705],[-81.601206,30.728141],[-81.542675,30.713593],[-81.530531,30.722858],[-81.489537,30.7261],[-81.472597,30.713312],[-81.444124,30.709714],[-81.42742,30.69802],[-81.443099,30.600938],[-81.442564,30.555189],[-81.434064,30.522569],[-81.447087,30.503679],[-81.440108,30.497678],[-81.42601,30.496739],[-81.410809,30.482039],[-81.407008,30.42204],[-81.397422,30.400626],[-81.396407,30.34004],[-81.385505,30.273841],[-81.308978,29.96944],[-81.295268,29.928614],[-81.270442,29.883106],[-81.256711,29.784693],[-81.240924,29.739218],[-81.163581,29.55529],[-80.966176,29.14796],[-80.709725,28.756692],[-80.574868,28.585166],[-80.560973,28.530736],[-80.525094,28.459454],[-80.526732,28.451705],[-80.562877,28.437779],[-80.587813,28.410856],[-80.606874,28.336484],[-80.604214,28.257733],[-80.589975,28.17799],[-80.566432,28.09563],[-80.508871,27.970477],[-80.383695,27.740045],[-80.350553,27.628361],[-80.330956,27.597541],[-80.311757,27.524625],[-80.30117,27.500314],[-80.293171,27.500314],[-80.253665,27.37979],[-80.16147,27.192814],[-80.153375,27.169308],[-80.159554,27.163325],[-80.093909,27.018587],[-80.031362,26.796339],[-80.03212,26.77153],[-80.037462,26.76634],[-80.032862,26.700842],[-80.038863,26.569347],[-80.060564,26.444652],[-80.079865,26.264358],[-80.089365,26.231859],[-80.108995,26.088372],[-80.117778,25.986369],[-80.119684,25.841043],[-80.127987,25.772245],[-80.144,25.740812],[-80.154972,25.66549],[-80.160903,25.664897],[-80.176916,25.685062],[-80.166241,25.72895],[-80.184626,25.745557],[-80.197674,25.74437],[-80.240376,25.724206],[-80.267065,25.651849],[-80.296719,25.622195],[-80.305615,25.593134],[-80.302057,25.567632],[-80.313918,25.539164],[-80.328746,25.53264],[-80.339421,25.499427],[-80.337049,25.465621],[-80.328152,25.443084],[-80.320442,25.437153],[-80.326373,25.422919],[-80.32578,25.39801],[-80.306801,25.384369],[-80.31036,25.3731],[-80.335269,25.338701],[-80.374116,25.31735],[-80.418872,25.235532],[-80.495341,25.199463],[-80.569124,25.190117],[-80.669236,25.137837],[-80.777499,25.135047],[-80.82653,25.160478],[-80.838227,25.174791],[-80.858167,25.176576],[-80.899459,25.162337],[-80.900559,25.139755],[-80.970727,25.134084],[-80.999176,25.124222],[-81.049308,25.128322],[-81.079859,25.118797],[-81.141024,25.163868],[-81.146737,25.193139],[-81.171265,25.221609],[-81.16207,25.289833],[-81.148915,25.318067],[-81.151916,25.324766],[-81.140099,25.341117],[-81.12141,25.33875],[-81.117265,25.354953],[-81.128492,25.380511],[-81.150508,25.387255],[-81.146765,25.407577],[-81.168652,25.463848],[-81.208201,25.504937],[-81.204389,25.538908],[-81.209321,25.548611],[-81.225557,25.55847],[-81.240519,25.599041],[-81.240677,25.613629],[-81.253951,25.638181],[-81.290328,25.687506],[-81.328935,25.717233],[-81.346078,25.721473],[-81.343984,25.747668],[-81.361875,25.772715],[-81.340406,25.786631],[-81.352731,25.822015],[-81.386127,25.839906],[-81.394476,25.851834],[-81.417536,25.864954],[-81.441391,25.863761],[-81.458487,25.868929],[-81.473992,25.888411],[-81.508979,25.884037],[-81.511762,25.89676],[-81.527665,25.901531],[-81.584519,25.888808],[-81.644553,25.897953],[-81.663821,25.885605],[-81.678287,25.845301],[-81.68954,25.85271],[-81.713172,25.897568],[-81.727086,25.907207],[-81.73195,25.931506],[-81.749724,25.960463],[-81.747834,25.994273],[-81.762439,26.00607],[-81.801663,26.088227],[-81.820675,26.236735],[-81.833142,26.294518],[-81.868983,26.378648],[-81.91171,26.427158],[-81.964212,26.457957],[-81.969509,26.476505],[-82.008961,26.484052],[-82.01368,26.490829],[-82.00908,26.505203],[-82.024604,26.512677],[-82.043577,26.519577],[-82.06715,26.513252],[-82.07175,26.492554],[-82.105672,26.48393],[-82.111996,26.54085],[-82.137869,26.637441],[-82.181565,26.681712],[-82.17984,26.696661],[-82.173516,26.701836],[-82.139019,26.702986],[-82.125795,26.699536],[-82.106247,26.667339],[-82.099922,26.662739],[-82.093023,26.665614],[-82.084974,26.702411],[-82.066575,26.742657],[-82.061401,26.789228],[-82.055076,26.802452],[-82.059101,26.876621],[-82.090723,26.888694],[-82.093023,26.906518],[-82.090148,26.923191],[-82.061976,26.931241],[-82.063126,26.950214],[-82.076349,26.958263],[-82.107972,26.957688],[-82.117171,26.954239],[-82.137294,26.926066],[-82.162017,26.925491],[-82.175241,26.916867],[-82.156267,26.851898],[-82.147068,26.789803],[-82.151093,26.783479],[-82.172941,26.778879],[-82.17869,26.772555],[-82.221812,26.77198],[-82.232193,26.78288],[-82.251134,26.755881],[-82.259867,26.717398],[-82.269499,26.784674],[-82.289086,26.827784],[-82.351649,26.908384],[-82.445718,27.060634],[-82.477019,27.141231],[-82.539719,27.254326],[-82.569754,27.279452],[-82.569248,27.298588],[-82.597629,27.335754],[-82.642821,27.38972],[-82.691821,27.437218],[-82.714521,27.500415],[-82.745748,27.538834],[-82.708121,27.523514],[-82.710621,27.501715],[-82.706821,27.498415],[-82.686421,27.497215],[-82.683621,27.513115],[-82.674621,27.519614],[-82.65072,27.523115],[-82.632053,27.551908],[-82.612019,27.571231],[-82.611717,27.585283],[-82.584629,27.596021],[-82.570607,27.608882],[-82.558538,27.638678],[-82.514265,27.705588],[-82.494891,27.718963],[-82.477638,27.723004],[-82.482305,27.742649],[-82.434635,27.764355],[-82.418401,27.803187],[-82.402857,27.812671],[-82.393383,27.837519],[-82.402615,27.882602],[-82.413915,27.901401],[-82.451591,27.907506],[-82.460016,27.9116],[-82.462078,27.920066],[-82.478063,27.92768],[-82.491117,27.9145],[-82.488057,27.863566],[-82.46884,27.843295],[-82.47244,27.822559],[-82.511193,27.828015],[-82.553946,27.848462],[-82.552918,27.862702],[-82.538618,27.864901],[-82.529918,27.877501],[-82.542818,27.890601],[-82.531318,27.9039],[-82.533718,27.932999],[-82.553918,27.966998],[-82.576003,27.969424],[-82.62959,27.998474],[-82.678606,27.993715],[-82.684793,27.971824],[-82.720522,27.955798],[-82.724122,27.948098],[-82.720395,27.937199],[-82.710022,27.928299],[-82.691621,27.924899],[-82.685121,27.916299],[-82.628063,27.910397],[-82.63422,27.9037],[-82.63212,27.8911],[-82.61002,27.873501],[-82.567919,27.883701],[-82.566819,27.858002],[-82.598443,27.857582],[-82.586519,27.816703],[-82.622723,27.779868],[-82.63052,27.753905],[-82.62572,27.727006],[-82.63362,27.710607],[-82.652521,27.700307],[-82.677321,27.706207],[-82.679019,27.696054],[-82.713629,27.698661],[-82.718822,27.692007],[-82.721622,27.663908],[-82.712555,27.646647],[-82.698091,27.638858],[-82.705017,27.62531],[-82.733076,27.612972],[-82.739122,27.636909],[-82.738022,27.706807],[-82.746223,27.731306],[-82.760923,27.745205],[-82.783124,27.783804],[-82.828561,27.822254],[-82.846526,27.854301],[-82.851126,27.8863],[-82.840882,27.937162],[-82.831388,27.962117],[-82.824875,27.960201],[-82.821975,27.956868],[-82.838484,27.909111],[-82.832155,27.909242],[-82.805462,27.960201],[-82.792635,28.01116],[-82.792635,28.032307],[-82.782724,28.055894],[-82.781324,28.127591],[-82.790724,28.15249],[-82.808474,28.154803],[-82.805097,28.172181],[-82.797762,28.187789],[-82.762643,28.219013],[-82.764103,28.244345],[-82.759072,28.25402],[-82.746188,28.261192],[-82.732792,28.291933],[-82.73146,28.325075],[-82.706112,28.368057],[-82.706322,28.401325],[-82.697433,28.420166],[-82.684137,28.428019],[-82.674787,28.441956],[-82.680396,28.457194],[-82.665055,28.484434],[-82.669416,28.519879],[-82.656694,28.544814],[-82.66165,28.554143],[-82.654138,28.590837],[-82.664055,28.606584],[-82.674665,28.647588],[-82.668889,28.694302],[-82.712373,28.720921],[-82.698281,28.75701],[-82.730245,28.850155],[-82.688864,28.905609],[-82.702618,28.932955],[-82.723861,28.953506],[-82.735754,28.973709],[-82.737872,28.995703],[-82.758906,28.993277],[-82.764055,28.999707],[-82.753513,29.026496],[-82.759704,29.054192],[-82.783328,29.064619],[-82.780558,29.07358],[-82.816925,29.076215],[-82.823659,29.098902],[-82.801166,29.105103],[-82.799117,29.110647],[-82.805703,29.129848],[-82.804736,29.146624],[-82.827073,29.158425],[-82.974676,29.17091],[-82.991653,29.180664],[-83.018212,29.151417],[-83.019071,29.141324],[-83.030453,29.134023],[-83.053207,29.130839],[-83.056867,29.146263],[-83.068249,29.153135],[-83.061162,29.176113],[-83.087839,29.21642],[-83.074734,29.247975],[-83.077265,29.255331],[-83.089013,29.266502],[-83.107477,29.268889],[-83.128027,29.282733],[-83.169576,29.290355],[-83.17826,29.327916],[-83.175518,29.34469],[-83.200702,29.373855],[-83.202446,29.394422],[-83.218075,29.420492],[-83.240509,29.433178],[-83.272019,29.432256],[-83.294747,29.437923],[-83.311546,29.475666],[-83.33113,29.475594],[-83.356722,29.499901],[-83.370288,29.499901],[-83.379254,29.503558],[-83.383973,29.512995],[-83.400252,29.517242],[-83.405256,29.578319],[-83.39948,29.612956],[-83.414701,29.670536],[-83.436259,29.677389],[-83.455356,29.676444],[-83.483143,29.690478],[-83.483567,29.698542],[-83.493728,29.708388],[-83.537645,29.72306],[-83.566018,29.761434],[-83.584716,29.77608],[-83.585899,29.811754],[-83.595493,29.827984],[-83.618568,29.842336],[-83.63798,29.886073],[-83.679219,29.918513],[-83.788729,29.976982],[-83.82869,29.983187],[-83.845427,29.998068],[-83.93151,30.039068],[-83.931879,30.044175],[-83.991607,30.08392],[-84.000716,30.096209],[-84.024274,30.103271],[-84.06299,30.101378],[-84.083057,30.092286],[-84.10273,30.093611],[-84.11384,30.085478],[-84.124889,30.090601],[-84.167881,30.071422],[-84.179149,30.073187],[-84.19853,30.087937],[-84.237014,30.08556],[-84.247491,30.10114],[-84.256439,30.103791],[-84.272511,30.092358],[-84.270792,30.068094],[-84.277168,30.060263],[-84.297836,30.057451],[-84.315344,30.069492],[-84.358923,30.058224],[-84.365882,30.024588],[-84.361962,29.987739],[-84.3477,29.984123],[-84.343041,29.9751],[-84.333746,29.923721],[-84.343389,29.899539],[-84.349066,29.896812],[-84.378937,29.893112],[-84.423834,29.902996],[-84.443652,29.913785],[-84.451705,29.929085],[-84.494562,29.913957],[-84.511996,29.916574],[-84.535873,29.910092],[-84.603303,29.876117],[-84.647958,29.847104],[-84.65645,29.834277],[-84.692053,29.829059],[-84.755595,29.78854],[-84.868271,29.742454],[-84.881777,29.733882],[-84.888031,29.722406],[-84.901781,29.735723],[-84.877111,29.772888],[-84.893992,29.785176],[-84.90413,29.786279],[-84.91511,29.783303],[-84.93837,29.750211],[-84.964007,29.742422],[-84.968841,29.72708],[-84.993264,29.714961],[-85.037212,29.711074],[-85.072123,29.719027],[-85.121473,29.715854],[-85.177284,29.700193],[-85.22745,29.693633],[-85.259719,29.681296],[-85.319215,29.681494],[-85.343619,29.672004],[-85.347711,29.66719],[-85.344768,29.654793],[-85.380303,29.698485],[-85.397871,29.740498],[-85.413983,29.799865],[-85.417971,29.828855],[-85.413575,29.85294],[-85.405815,29.865817],[-85.392469,29.870914],[-85.405011,29.830151],[-85.405907,29.80193],[-85.37796,29.709621],[-85.353885,29.684765],[-85.317661,29.691286],[-85.31139,29.697557],[-85.302591,29.808094],[-85.31142,29.814373],[-85.317464,29.838894],[-85.336654,29.849295],[-85.363731,29.898915],[-85.405052,29.938487],[-85.425956,29.949888],[-85.487764,29.961227],[-85.509148,29.971466],[-85.571907,30.02644],[-85.588242,30.055543],[-85.601178,30.056342],[-85.69681,30.09689],[-85.775405,30.15629],[-85.9226,30.238024],[-86.089963,30.303569],[-86.222561,30.343585],[-86.2987,30.363049],[-86.412076,30.380346],[-86.50615,30.3823],[-86.632953,30.396299],[-86.750906,30.391881],[-86.909679,30.372423],[-87.206254,30.320943],[-87.267827,30.31548],[-87.295422,30.323503],[-87.518324,30.280435],[-87.452378,30.300201],[-87.450078,30.3111],[-87.50278,30.307301],[-87.504701,30.324039],[-87.49998,30.328957],[-87.459978,30.3363],[-87.452278,30.344099],[-87.451878,30.364999],[-87.438678,30.380798],[-87.440678,30.391498],[-87.429578,30.406498],[-87.403477,30.410198],[-87.366591,30.436648],[-87.370768,30.446865],[-87.399877,30.450997],[-87.425078,30.465596],[-87.434678,30.479196],[-87.431178,30.495795],[-87.447702,30.510458],[-87.446586,30.527068],[-87.43544,30.54914],[-87.418647,30.561837],[-87.406558,30.599928],[-87.397308,30.608728],[-87.393588,30.63088],[-87.397262,30.654351],[-87.406958,30.675165],[-87.449362,30.698913],[-87.466338,30.700835],[-87.481225,30.716508],[-87.502317,30.72159],[-87.511729,30.733535],[-87.532607,30.743489],[-87.545044,30.778666],[-87.581869,30.812403],[-87.600486,30.820627],[-87.605776,30.831304],[-87.615923,30.834693],[-87.634938,30.865886],[-87.592055,30.951492],[-87.589187,30.964464],[-87.599172,30.995722],[-87.571281,30.99787],[-85.998643,30.99287],[-85.002368,31.000682],[-85.004026,30.973468],[-84.980127,30.961286],[-84.983627,30.936986],[-84.971026,30.928187],[-84.936828,30.884683],[-84.935256,30.854328],[-84.928335,30.844263],[-84.936042,30.820671],[-84.928323,30.79309],[-84.918023,30.77809],[-84.920123,30.76599],[-84.914322,30.753591],[-84.896122,30.750591],[-84.864693,30.711542],[-83.309455,30.634417]]]]},\"properties\":{\"name\":\"Florida\",\"nation\":\"USA \"}}]}","volume":"2","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swindall, Jessica E.","contributorId":219304,"corporation":false,"usgs":false,"family":"Swindall","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":786427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ober, Holly K.","contributorId":219305,"corporation":false,"usgs":false,"family":"Ober","given":"Holly","email":"","middleInitial":"K.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":786428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":206817,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":786429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carthy, Raymond R. 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":223853,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","email":"","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":786430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228433,"text":"70228433 - 2020 - Estimating population persistence for at-risk species using citizen science data","interactions":[],"lastModifiedDate":"2022-02-10T13:24:33.608697","indexId":"70228433","displayToPublicDate":"2020-03-03T07:22:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating population persistence for at-risk species using citizen science data","docAbstract":"Population persistence probability is valuable for characterizing risk to species and informing listing and conservation decisions but is challenging to estimate through traditional methods for rare, data-limited species. Modeling approaches have used citizen science data to mitigate data limitations of focal species and better estimate parameters such as occupancy and detection, but their use to estimate persistence and inform conservation decisions is limited. We developed an approach to estimate persistence using only occurrence records of the target species and citizen science occurrence data of non-target species to account for search effort and imperfect detection. We applied the approach to a highly cryptic and data-limited species, the southern hognose snake (Heterodon simus), as part of its USFWS Species Status Assessment, and estimated current (in 2018) and future persistence under plausible scenarios of varying levels of urbanization, sea level rise, and management. Of 222 known populations, 133 (60%) are likely extirpated currently (persistence probability < 50%), and 165 (74%) populations are likely to be extirpated by 2080 with no additional management. Future management scenarios that included strategies to acquire and improve habitat on currently unprotected lands with existing populations lessened the estimated rate of population declines. These results can directly inform listing decisions and conservation planning for the southern hognose snake by Federal, State, and other partners. Our approach – using occurrence records and auxiliary data from non-target species to estimate population persistence – is applicable across rare and at-risk species for evaluating extinction risk with limited data and prioritizing management actions.
Several species of ciscoes Coregonus, subgenus Leucichthys that are native to the Laurentian Great Lakes are rare or extirpated. The restoration of Coregonus fishes is being actively pursued through stocking, and success may depend on the availability of unoccupied niche space. We described the spring–summer habitat occupancy and diets of three native cisco species (Bloater Coregonus hoyi, Cisco C. artedi, and Kiyi C. Kiyi) and invasive Rainbow Smelt Osmerus mordax in Lake Superior and measured niche overlap among these species for both small and large sizes. The potential habitat area was highest for Cisco and Kiyi, followed by Bloater and Rainbow Smelt. The probability of overlap in habitat occupation, as measured by bathymetric depth, fish capture depth, distance from shore, and fish capture water temperature was highest for small Rainbow Smelt and Cisco. Trophic overlap, as measured by stomach contents and stable isotopes, was highest between small Bloater and Cisco and between large Bloater and Kiyi. All of the species showed significant ontogenetic change in both habitat occupation and diet. The overall niche overlap in spring–summer habitat and diet was greatest between small Cisco and Rainbow Smelt and between large Bloater and Kiyi; however, differences in individual niche dimensions likely limit competition for both species pairs. Synthesizing the diet and habitat niche data revealed nuanced niches that allow these seemingly similar planktivorous species to coexist. Kiyi had the least niche overlap with other cisco species and Rainbow Smelt, so from an available niche perspective Kiyi would be a strong candidate for reintroduction into lakes from which they were extirpated.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10219","usgsCitation":"Rosinski, C.L., Vinson, M., and Yule, D.L., 2020, Niche partitioning among native ciscoes and nonnative Rainbow Smelt in Lake Superior: Transactions of the American Fisheries Society, v. 149, no. 2, p. 184-203, https://doi.org/10.1002/tafs.10219.","productDescription":"10 p.","startPage":"184","endPage":"203","ipdsId":"IP-113030","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":382868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.11035156249999,\n 49.009050809382046\n ],\n [\n -89.1650390625,\n 48.574789910928864\n ],\n [\n -89.4287109375,\n 48.019324184801185\n ],\n [\n -90.703125,\n 47.724544549099676\n ],\n [\n -92.1533203125,\n 46.6795944656402\n ],\n [\n -90.8349609375,\n 46.9502622421856\n ],\n [\n -90.8349609375,\n 46.558860303117164\n ],\n [\n -90,\n 46.76996843356982\n ],\n [\n -88.9892578125,\n 47.07012182383309\n ],\n [\n -87.978515625,\n 47.338822694822\n ],\n [\n -88.505859375,\n 46.76996843356982\n ],\n [\n -88.11035156249999,\n 46.9502622421856\n ],\n [\n -87.451171875,\n 46.558860303117164\n ],\n [\n -86.3525390625,\n 46.46813299215554\n ],\n [\n -85.4736328125,\n 46.70973594407157\n ],\n [\n -85.0341796875,\n 46.70973594407157\n ],\n [\n -84.8583984375,\n 46.31658418182218\n ],\n [\n -84.3310546875,\n 46.49839225859763\n ],\n [\n -84.5068359375,\n 47.07012182383309\n ],\n [\n -84.90234375,\n 47.989921667414194\n ],\n [\n -85.95703125,\n 48.10743118848039\n ],\n [\n -86.3525390625,\n 48.719961222646276\n ],\n [\n -88.11035156249999,\n 49.009050809382046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosinski, Caroline Lynn 0000-0003-3635-2748","orcid":"https://orcid.org/0000-0003-3635-2748","contributorId":248618,"corporation":false,"usgs":true,"family":"Rosinski","given":"Caroline","email":"","middleInitial":"Lynn","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809625,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809626,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211932,"text":"70211932 - 2020 - Mercury export from Arctic great rivers","interactions":[],"lastModifiedDate":"2020-08-11T21:05:02.516262","indexId":"70211932","displayToPublicDate":"2020-03-02T16:04:25","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":"Mercury export from Arctic great rivers","docAbstract":"Land–ocean linkages are strong across the circumpolar north, where the Arctic Ocean accounts for 1% of the global ocean volume and receives more than 10% of the global river discharge. Yet estimates of Arctic riverine mercury (Hg) export constrained from direct Hg measurements remain sparse. Here, we report results from a coordinated, year-round sampling program that focused on the six major Arctic rivers to establish a contemporary (2012–2017) benchmark of riverine Hg export. We determine that the six major Arctic rivers exported an average of 20 000 kg y–1 of total Hg (THg, all forms of Hg). Upscaled to the pan-Arctic, we estimate THg flux of 37 000 kg y–1. More than 90% of THg flux occurred during peak river discharge in spring and summer. Normalizing fluxes to watershed area (yield) reveals higher THg yields in regions where greater denudation likely enhances Hg mobilization. River discharge, suspended sediment, and dissolved organic carbon predicted THg concentration with moderate fidelity, while suspended sediment and water yields predicted THg yield with high fidelity. These findings establish a benchmark in the face of rapid Arctic warming and an intensifying hydrologic cycle, which will likely accelerate Hg cycling in tandem with changing inputs from thawing permafrost and industrial activity.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.9b07145","usgsCitation":"Zolkos, S., Krabbenhoft, D.P., Suslova, A., Tank, S.E., McClelland, J.W., Spencer, R.G., Shiklomanov, A., Zhulidov, A.V., Gurtovaya, T., Zimov, N., Zimov, S., Mutter, E., Kutny, L., Amos, E., and Holmes, R.M., 2020, Mercury export from Arctic great rivers: Environmental Science & Technology, v. 54, no. 7, p. 4140-4148, https://doi.org/10.1021/acs.est.9b07145.","productDescription":"9 p.","startPage":"4140","endPage":"4148","ipdsId":"IP-115773","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Russia, United States","volume":"54","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zolkos, Scott 0000-0001-9945-6945","orcid":"https://orcid.org/0000-0001-9945-6945","contributorId":238024,"corporation":false,"usgs":false,"family":"Zolkos","given":"Scott","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":795853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suslova, Anya","contributorId":238025,"corporation":false,"usgs":false,"family":"Suslova","given":"Anya","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tank, Suzanne E. 0000-0002-5371-6577","orcid":"https://orcid.org/0000-0002-5371-6577","contributorId":238026,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":47684,"text":"Department of Biological Sciences, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":795855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClelland, James W. 0000-0001-9619-8194","orcid":"https://orcid.org/0000-0001-9619-8194","contributorId":238027,"corporation":false,"usgs":false,"family":"McClelland","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":795856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. M. 0000-0003-0777-0748","orcid":"https://orcid.org/0000-0003-0777-0748","contributorId":238028,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert","email":"","middleInitial":"G. M.","affiliations":[{"id":47686,"text":"Department of Earth, Ocean and Atmospheric Science, Florida State University","active":true,"usgs":false}],"preferred":false,"id":795857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shiklomanov, Alexander","contributorId":238029,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexander","affiliations":[{"id":47687,"text":"Institute for the Study of Earth, Oceans, and Space, University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":795858,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhulidov, Alexander V.","contributorId":238030,"corporation":false,"usgs":false,"family":"Zhulidov","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795859,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gurtovaya, Tatiana","contributorId":238031,"corporation":false,"usgs":false,"family":"Gurtovaya","given":"Tatiana","email":"","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795860,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimov, Nikita","contributorId":238032,"corporation":false,"usgs":false,"family":"Zimov","given":"Nikita","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795861,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimov, Sergey","contributorId":238033,"corporation":false,"usgs":false,"family":"Zimov","given":"Sergey","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795862,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mutter, Edda A.","contributorId":238034,"corporation":false,"usgs":false,"family":"Mutter","given":"Edda A.","affiliations":[{"id":47690,"text":"˚Yukon River Inter-Tribal Watershed Council, Anchorage, Alaska","active":true,"usgs":false}],"preferred":false,"id":795863,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kutny, Les","contributorId":238035,"corporation":false,"usgs":false,"family":"Kutny","given":"Les","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795864,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Amos, Edwin","contributorId":238036,"corporation":false,"usgs":false,"family":"Amos","given":"Edwin","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795865,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Holmes, Robert M.","contributorId":178901,"corporation":false,"usgs":false,"family":"Holmes","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":795866,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70208879,"text":"70208879 - 2020 - Gas hydrate petroleum systems: What constitutes the “seal”?","interactions":[],"lastModifiedDate":"2020-06-04T16:58:08.036025","indexId":"70208879","displayToPublicDate":"2020-03-02T15:50:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Gas hydrate petroleum systems: What constitutes the “seal”?","docAbstract":"The gas hydrate petroleum system (GHPS) approach, which has been used to characterize gas hydrates in nature, utilizes three distinct components: a methane source, a methane migration pathway, and a reservoir that not only contains gas hydrate, but also acts as a seal to prevent methane loss. Unlike GHPS, a traditional petroleum system (PS) approach further distinguishes between the reservoir, a unit with generally coarser sediment grains, and a separate overlying seal unit with generally finer sediment grains. Adopting this traditional PS distinction in the GHPS approach facilitates assessments of reservoir growth and production potential. The significance of the seal for the formation of a gas hydrate reservoir as well as for the efficiency in methane extraction from the reservoir as an energy resource is evident in the findings from recent offshore field expeditions, such as India’s second National Gas Hydrate Program expedition (NGHP-02). In regards to gas hydrate-bearing reservoir formation, the NGHP-02 gas chemistry data indicate a primarily microbial methane source. Fine-grained seal sediment in contact with coarser-grained reservoir sediment can facilitate that microbial methane production. Logging-while-drilling and sediment core data also indicate that the overlying fine-grained seal sediment is less permeable than the underlying, highly gas hydrate-saturated reservoir sediment. The overlying seal’s capacity to act as a low-permeability boundary is important not only for preventing methane migration out of the reservoir over time, but for also preventing water invasion into the reservoir during methane extraction from the reservoir. Ultimately, the presence of an overlying, fine-grained, low-permeability “Seal”? influences how gas hydrate initially forms in a coarse-grained reservoir and dictates how efficiently methane can be extracted as an energy resource from the gas hydrate reservoir via depressurization.","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/int-2019-0026.1","usgsCitation":"Jang, J., Waite, W., and Stern, L.A., 2020, Gas hydrate petroleum systems: What constitutes the “seal”?: Interpretation, v. 8, no. 2, p. T231-T248, https://doi.org/10.1190/int-2019-0026.1.","productDescription":"18 p.","startPage":"T231","endPage":"T248","ipdsId":"IP-104479","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Bay of Bengal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 84.44091796875,\n 18.47960905583197\n ],\n [\n 82.41943359375,\n 17.11979250078707\n ],\n [\n 82.44140625,\n 16.720385051694\n ],\n [\n 82.1337890625,\n 16.172472808397515\n ],\n [\n 81.40869140625,\n 16.25686733062344\n ],\n [\n 81.10107421874999,\n 15.665354182093287\n ],\n [\n 82.90283203125,\n 14.817370620155254\n ],\n [\n 86.396484375,\n 17.434510551522894\n ],\n [\n 84.44091796875,\n 18.47960905583197\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jang, Junbong 0000-0001-5500-7558 jjang@usgs.gov","orcid":"https://orcid.org/0000-0001-5500-7558","contributorId":189400,"corporation":false,"usgs":true,"family":"Jang","given":"Junbong","email":"jjang@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stern, Laura A. 0000-0003-3440-5674","orcid":"https://orcid.org/0000-0003-3440-5674","contributorId":212238,"corporation":false,"usgs":true,"family":"Stern","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":783812,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211218,"text":"70211218 - 2020 - Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda","interactions":[],"lastModifiedDate":"2020-07-20T12:55:11.299767","indexId":"70211218","displayToPublicDate":"2020-03-02T15:31:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda","docAbstract":"Part of the spatial variation in the apparent sea-level record of the last interglacial (LIG) period is due to the diverse response of coastlines to glacial isostatic adjustment (GIA) processes, particularly where coastlines were close to the Laurentide Ice Sheet during the past two glacial periods. We tested modeled LIG paleo-sea levels on New Providence Island (NPI), Bahamas and Bermuda by investigating emergent coral patch reefs and oolitic/peloidal beach deposits. Corals with closed-system histories collected from patch reefs on NPI have ages of 128-118 ka and ooids/peloids from beach ridges have closed-system ages of 128-116 ka. Elevations of patch reefs indicate a LIG paleo-sea level of at least ∼7 m to ∼9 m above present. Beach ridge sediments indicate paleo-sea levels of ∼5 m to ∼14 m (assuming subsidence, ∼7 m to ∼16 m) above present during the LIG. Some, though not all of these measurements are in good agreement with GIA models of paleo-sea level that have been simulated for the Bahamas. On Bermuda, corals with closed-system histories collected from marine deposits have ages of 126-114 ka. Although coral-bearing marine deposits on Bermuda lack the precise indication of paleo-sea level provided by patch reefs and oolitic beach ridges, these sediments nevertheless provide at least a first-order estimate of paleo-sea level. Paleo-sea level records on Bermuda are consistently lower (∼2 m to ∼7 m) than what GIA models simulate for the LIG. The reason for the reasonable agreement with models for the Bahamas and poor agreement for Bermuda is not understood, but needs further investigation in light of the probability of a higher sea level in the near future.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106212","usgsCitation":"Muhs, D., Simmons, K., Schumann, R.R., Schweig, E.S., and Rowe, M.P., 2020, Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda: Quaternary Science Reviews, v. 233, 106212, 28 p., https://doi.org/10.1016/j.quascirev.2020.106212.","productDescription":"106212, 28 p.","ipdsId":"IP-112522","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457526,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1602345","text":"Publisher Index Page"},{"id":376496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bahamas, Bermuda","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.07476806640625,\n 31.98944183792288\n ],\n [\n -64.48699951171875,\n 31.98944183792288\n ],\n [\n -64.48699951171875,\n 32.55838861348271\n ],\n [\n -65.07476806640625,\n 32.55838861348271\n ],\n [\n -65.07476806640625,\n 31.98944183792288\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.21142578125,\n 23.46324633155036\n ],\n [\n -75.21240234375,\n 23.46324633155036\n ],\n [\n -75.21240234375,\n 27.196014383173306\n ],\n [\n -79.21142578125,\n 27.196014383173306\n ],\n [\n -79.21142578125,\n 23.46324633155036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"233","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simmons, Kathleen R. 0000-0002-7920-094X","orcid":"https://orcid.org/0000-0002-7920-094X","contributorId":229460,"corporation":false,"usgs":false,"family":"Simmons","given":"Kathleen R.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":793242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schumann, R. Randall 0000-0001-8158-6960 rschumann@usgs.gov","orcid":"https://orcid.org/0000-0001-8158-6960","contributorId":1569,"corporation":false,"usgs":true,"family":"Schumann","given":"R.","email":"rschumann@usgs.gov","middleInitial":"Randall","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schweig, Eugene S. III 0000-0003-3669-9741","orcid":"https://orcid.org/0000-0003-3669-9741","contributorId":229461,"corporation":false,"usgs":false,"family":"Schweig","given":"Eugene","suffix":"III","email":"","middleInitial":"S.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":793244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowe, Mark P.","contributorId":229462,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark","email":"","middleInitial":"P.","affiliations":[{"id":41653,"text":"Bermuda","active":true,"usgs":false}],"preferred":false,"id":793245,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209070,"text":"70209070 - 2020 - Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018","interactions":[],"lastModifiedDate":"2020-03-16T17:06:02","indexId":"70209070","displayToPublicDate":"2020-03-02T14:40:52","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018","docAbstract":"We sampled juvenile wild Steelhead Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize populations and investigate life-history metrics, particularly migratory patterns. We used Passive Integrated Transponder (PIT)-tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead have been planted in the Wind River subbasin since 1997, and hatchery adults are estimated to be less than one percent of spawners in most years (pers comm. Thomas Buehrens, Washington Department of Fish and Wildlife). Our repeated headwater sampling of consistent sites in the Wind River subbasin has also allowed us to track relative abundance of Brook Trout, a non-native species to the Wind River. Our work is contributing to understanding of Steelhead population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, where our PTISs are helping to quantify adult response.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research Monitoring and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (www.cbfish.org/ProgramStrategy.mvc/ViewProgramStrategySummary/1). Specifically this work addresses the sub-strategies of: 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and to Uncertainties Research regarding differing life histories of a wild Steelhead population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives. Our headwaters parr PIT tagging, WDFW parr, smolt, and adult tagging and our instream PTISs are providing data on movements and life histories of parr, smolt, and adult Steelhead.
During summer 2018, we PIT-tagged 1,592 age-0 and age-1 Steelhead parr in headwater areas of the Wind River subbasin to characterize population traits and investigate life-history diversity, including growth and pre-smolt downstream movement. Repeat headwater sampling and smolt trap operations provide opportunities for recapture, and instream PTISs and Columbia River infrastructure provide opportunity for detection of PIT-tagged fish. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead parr, smolts, and adults.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. Substantial numbers of parr make downstream movements as age-1 fish. We have estimated that from 15 to 33 percent of parr tagged as age-0 fish make downstream migrations at age-1 for additional rearing. We have estimated that from 1 to 27 percent of parr tagged as age-1 fish make downstream migrations during fall. These findings raise many questions about parr rearing strategies, habitat use, and success of these migrants and suggest a need for broader monitoring of juvenile Steelhead in some river systems to fully document juvenile production. Long-term monitoring of PIT-tagged fish is providing information on contribution of various life-history strategies to smolt production and adult returns.
Movements of PIT-tagged adult Steelhead were recorded at instream PTISs. These data have allowed assessment of adult returns to tributary watersheds within the Wind River subbasin. Detection efficiency of adult PIT-tagged Steelhead at our primary adult-monitoring PTIS in Trout Creek has been greater than 92 percent during 6 of the past 7 years. This is providing excellent data to estimate adult returns to this watershed. Determination of adult use of tributary watersheds is providing data to help evaluate the efficacy of the removal of Hemlock Dam on Trout Creek. Hemlock Dam, located at rkm 2.0 of Trout Creek, was removed in summer 2009. The dam contributed to hydrologic impairment of Trout Creek and had potential negative effects on Steelhead. The improvements made to the upper Wind River PTIS (site code WRU at rkm 28.3; better site characteristics and grid power) during 2016 and 2017, and a planned new site in the Mine Reach of the upper Wind River, will allow estimates of subbasin adult escapement like those in Trout Creek.
During 2018, we also completed planning and permitting with U.S. Forest Service for a new PTIS site at rkm 36 of the Wind River (the Mine Reach, mentioned above). This site will replace two sites (one in Paradise Creek and one at rkm 41 of the Wind River), which had operational challenges due to lack of adequate solar power and winter difficulties. The new Mine Reach PTIS site at rkm 36, will have better solar exposure, fewer winter operations difficulties, and provide opportunity to detect fish from juvenile sampling sites that were downstream of the previous two PTISs. The more consistent operation of the new Mine Reach PTIS site will increase our ability to estimate migrant abundance as all the juveniles tagged upstream of it will be subject to the same potential detection history, instead of three different potential detection histories as before. Additionally, with the new Mine Reach PTIS site lower in the watershed, it will subject more PIT-tagged adult Steelhead to detection and provide ability to generate a nonbiased adult-detection efficiency estimate for the WRU PTIS at rkm 28.3 of the Wind River. This will provide the opportunity to estimate yearly adult Steelhead abundance to the upper Wind watershed area. Permitting is complete and some supplies have been purchased to build and install this new site in 2019.
Repeat sampling at consistent locations in the subbasin has allowed investigation into juvenile Steelhead growth patterns. Growth rates (relative change in weight) of age-0 PIT-tagged parr during summer are similar across the subbasin, but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly growth for parr tagged at age-0 is similar across the subbasin. Yearly growth for parr tagged at age-1 is lowest in Martha Creek, but similar elsewhere.
Non-native Brook Trout are present in portions of the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Percentage of catch that is Brook Trout at each of four sample sites in Trout Creek have declined from the period 1998 – 2003 to the period 2011 – 2018. There was a pattern of decline in percent of catch and number of Brook Trout at the Trout Creek sites from 2011 through 2016, though a slight upward trend during 2017 and 2018 has been evident.
Evaluating and planning restoration efforts are of interest to many managers and agencies to ensure efficient use of resources. The evaluation of various life-histories of Steelhead within the Wind River subbasin will provide information to better track populations, and to direct habitat restoration and water allocation planning. Movement of Steelhead parr raises many questions regarding estimating juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead parr are increasingly allowing us to investigate such questions. Increasingly detailed Viable Salmonid Population information, such as that provided by PIT-tagging and instream PTISs networks like those in the Wind River subbasin, provide data to inform policy and management, as life-history strategies and production bottlenecks are identified and understood.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I.G., 2020, Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018, 74 p.","productDescription":"74 p.","ipdsId":"IP-115314","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373226,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P170098"}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.34374999999999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 45.69083283645816\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian G. 0000-0002-3842-3485 ijezorek@usgs.gov","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":3572,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","email":"ijezorek@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784716,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205095,"text":"sir20195080 - 2020 - Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18","interactions":[],"lastModifiedDate":"2022-04-22T21:26:12.93031","indexId":"sir20195080","displayToPublicDate":"2020-03-02T10:35:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5080","displayTitle":"Assessment of Bridge Scour Countermeasures at Selected Bridges in the United States, 2014–18","title":"Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18","docAbstract":"Erosion of the streambed, known also as scour, around pier 3 of the New York State Thruway bridge over Schoharie Creek caused the pier to fail, which ultimately resulted in bridge failure during the flooding event of April 5, 1987. The Federal Highway Administration (FHWA) responded to the need for better guidance on the evaluation of bridge scour and the selection and installation of scour countermeasures with the release of several Hydraulic Engineering Circulars. Although this information has been available, used, and updated over the years, an evaluation of the current conditions of scour countermeasures has not been performed. Therefore, the U.S. Geological Survey, in cooperation with the FHWA, began a study in 2013 to assess the current conditions of bridge scour countermeasures at selected sites around the country. The bridge scour countermeasure site assessments included reviewing countermeasure design plans, field inspections, traditional surveys, motion-compensated terrestrial light detection and ranging technology (lidar), high-resolution multi-beam bathymetry scanning, underwater video imaging, and a review of the peak and daily streamflow history for the associated river or stream. A total of 34 bridge scour countermeasure sites were selected in 11 states for this study. The types of countermeasures installed at the bridge scour study sites ranged from riprap, the most common countermeasure in the study, to A-Jacks and cabled-concrete mattresses.
The installed countermeasures were generally exposed to hydraulic forces from floods that equaled or exceeded the 1-percent, and even the 0.2-percent, annual exceedance probability at some of the study sites, but not all. The field inspections and countermeasure evaluations identified areas of shifting, slumping, and some scour holes and damage or washouts to the countermeasures, but generally most remained in place. The high-resolution laser scanner data, photo imaging and traditional survey data, and field notes were provided to the FHWA for expert evaluation of the bridge scour countermeasure performance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195080","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Suro, T.P., Huizinga, R.J., Fosness, R.L., and Dudunake, T.J., 2020, Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18: U.S. Geological Survey Scientific Investigations Report 2019–5080, 29 p., https://doi.org/10.3133/sir20195080.","productDescription":"Report: ix, 29 p.; 2 Data Releases","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108279","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":372684,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5080/coverthb.jpg"},{"id":372685,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5080/sir20195080.pdf","text":"Report","size":"9.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5080"},{"id":372686,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71R6NQ2","text":"USGS data release","linkHelpText":"Geospatial Data for Bridge Scour Countermeasure Assessments at Select Bridges in the United States, 2014–16"},{"id":372687,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WW7G4W","text":"USGS data release","linkHelpText":"Geospatial Data for Bridge Scour Countermeasure Assessments at Select Bridges in the United States, 2016–18"},{"id":399537,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109736.htm"}],"country":"United States","state":"Alabama, Florida, Idaho, Illinois, Indiana, Iowa, Minnesota, Missouri, Montana, New Jersey, New York, Pennsylvania, South Carolina, Tennessee","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-85.605165,34.984678],[-85.188741,32.889727],[-84.93868,32.300708],[-85.141831,31.839261],[-85.041881,31.544684],[-85.096763,31.225651],[-84.864693,30.711542],[-82.287343,30.573458],[-82.101416,30.366556],[-81.962739,30.813636],[-81.472597,30.713312],[-81.256711,29.784693],[-80.567361,28.562353],[-80.566432,28.09563],[-80.031362,26.796339],[-80.152896,25.702855],[-80.229107,25.732509],[-80.495341,25.199463],[-81.079859,25.118797],[-81.362272,25.824401],[-81.727086,25.907207],[-81.868983,26.378648],[-82.094748,26.48393],[-82.076349,26.958263],[-82.147068,26.789803],[-82.301736,26.841588],[-82.714521,27.500415],[-82.393383,27.837519],[-82.716522,27.958398],[-82.566819,27.858002],[-82.721622,27.663908],[-82.851126,27.8863],[-82.674787,28.441956],[-82.702618,28.932955],[-83.679219,29.918513],[-84.245668,30.093021],[-84.335953,29.912962],[-85.343619,29.672004],[-85.405052,29.938487],[-86.2987,30.363049],[-88.014572,30.222366],[-87.766626,30.262353],[-88.008396,30.684956],[-88.115432,30.35657],[-88.341345,30.38947],[-88.468879,31.930262],[-88.097888,34.892202],[-88.172102,34.955213],[-90.309297,34.995694],[-90.09061,35.118287],[-90.166594,35.274588],[-89.992975,35.560774],[-89.923161,35.514428],[-89.915491,35.754917],[-89.68182,35.88999],[-89.86901,35.99964],[-90.368718,35.995812],[-90.075934,36.281485],[-90.157136,36.484317],[-94.617919,36.499414],[-94.605734,39.122204],[-95.082714,39.516712],[-94.876344,39.806894],[-95.382957,40.027112],[-95.870481,40.71248],[-95.929889,41.415155],[-96.096186,41.547192],[-96.077543,41.777824],[-96.628741,42.757532],[-96.448134,43.104452],[-96.598396,43.495074],[-96.453049,43.500415],[-96.452948,45.268925],[-96.835451,45.586129],[-96.587093,45.816445],[-96.559271,46.058272],[-96.789572,46.639079],[-96.851293,47.589264],[-97.139497,48.153108],[-97.108655,48.691484],[-97.238387,48.982631],[-95.153711,48.998903],[-95.153314,49.384358],[-94.974286,49.367738],[-94.555835,48.716207],[-93.741843,48.517347],[-92.984963,48.623731],[-92.634931,48.542873],[-92.698824,48.494892],[-92.341207,48.23248],[-92.066269,48.359602],[-91.542512,48.053268],[-90.88548,48.245784],[-90.703702,48.096009],[-89.489226,48.014528],[-90.735927,47.624343],[-92.058888,46.809938],[-92.025789,46.710839],[-92.189091,46.717541],[-92.29353,46.113824],[-92.869193,45.717568],[-92.646602,45.441635],[-92.807362,44.758909],[-91.351688,43.914545],[-91.07371,43.274746],[-91.174692,43.038713],[-91.05481,42.744686],[-90.617731,42.508077],[-87.815872,42.49192],[-87.812461,42.232278],[-87.365439,41.629536],[-86.824828,41.76024],[-84.825196,41.75999],[-84.803919,41.435531],[-84.816506,38.80532],[-85.448862,38.713368],[-85.415272,38.555416],[-85.816164,38.282969],[-86.042354,37.958018],[-86.33281,38.182938],[-86.634271,37.843845],[-86.810913,37.99715],[-87.065388,37.810481],[-87.402632,37.942267],[-87.666522,37.827455],[-87.921744,37.907885],[-88.158374,37.639948],[-88.063311,37.515755],[-88.450127,37.411717],[-88.490068,37.067874],[-89.058036,37.188767],[-89.171881,37.068184],[-89.202607,36.601576],[-89.395909,36.559649],[-88.045304,36.504081],[-88.068208,36.659747],[-87.872062,36.665089],[-81.6469,36.611918],[-82.02664,36.130222],[-82.325169,36.119363],[-82.531292,35.972188],[-82.701065,36.034404],[-82.955751,35.809802],[-83.880074,35.518745],[-84.052612,35.269982],[-84.28252,35.227877],[-84.321869,34.988408],[-85.605165,34.984678]]],[[[-81.582923,24.658732],[-81.451267,24.747464],[-81.298028,24.656774],[-81.765993,24.552103],[-81.582923,24.658732]]],[[[-84.777208,29.707398],[-84.696726,29.76993],[-85.036219,29.588919],[-84.777208,29.707398]]],[[[-82.255777,26.703437],[-82.038403,26.456907],[-82.186441,26.489221],[-82.255777,26.703437]]],[[[-80.250581,25.34193],[-80.611693,24.93842],[-80.192336,25.473331],[-80.250581,25.34193]]],[[[-111.044156,43.020052],[-111.046689,42.001567],[-116.969156,41.998991],[-117.026634,43.808104],[-116.925392,44.191544],[-117.243027,44.390974],[-116.463635,45.602785],[-116.84355,45.892273],[-117.03445,46.34132],[-117.032351,48.999188],[-104.048736,48.999877],[-104.040128,44.999987],[-111.044275,45.001345],[-111.044156,43.020052]]],[[[-79.761951,42.26986],[-78.868556,42.770258],[-79.061388,43.251349],[-78.370221,43.376505],[-77.577223,43.243263],[-76.794708,43.309632],[-76.235834,43.529256],[-76.133697,43.940356],[-76.360306,44.070907],[-76.312647,44.199044],[-75.26825,44.855119],[-74.868663,45.001274],[-73.343124,45.01084],[-73.430325,43.590532],[-73.247631,43.51924],[-73.276421,42.746019],[-73.508142,42.086257],[-73.482709,41.21276],[-73.727775,41.100696],[-73.782577,40.837601],[-72.635374,40.990536],[-72.245348,41.161217],[-72.273657,41.051533],[-72.116368,40.999796],[-71.869558,41.075046],[-73.23914,40.6251],[-73.934512,40.545175],[-74.143387,40.641903],[-74.209788,40.447407],[-73.995683,40.468707],[-73.971381,40.371709],[-74.141733,39.689435],[-74.933571,38.928519],[-74.905181,39.174945],[-75.165979,39.201842],[-75.542894,39.470447],[-75.481242,39.829112],[-75.799563,39.721882],[-80.519342,39.721403],[-80.519345,41.929168],[-79.761951,42.26986]]],[[[-74.144428,40.53516],[-74.219787,40.502603],[-74.120186,40.642201],[-74.144428,40.53516]]],[[[-79.290754,33.110051],[-80.413487,32.470672],[-80.749091,32.140137],[-81.066906,32.090351],[-81.511245,33.027786],[-82.554497,33.943819],[-82.854434,34.432275],[-83.353238,34.728648],[-83.008639,35.027595],[-82.257515,35.198636],[-81.043625,35.149877],[-80.684074,34.818907],[-79.692948,34.804973],[-78.580378,33.884925],[-79.084588,33.483669],[-79.290754,33.110051]]]]},\"properties\":{\"name\":\"Alabama\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ 08648
One of the objectives of the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Project is to assess groundwater quality in aquifers that are important sources of drinking water such as the coastal lowlands aquifer system, which is often referred to in Texas as the “Gulf Coast aquifer system.” The Gulf Coast aquifer system extends from Louisiana to Mexico and is a source of groundwater for several cities including Houston, Tex. The NAWQA groundwater studies in Texas in 2013–15 that assessed the Gulf Coast aquifer system included Principal Aquifer Surveys (PAS), Major Aquifer Studies (MAS), and Land Use Studies (LUS). These three study types are based on sampling networks of wells distributed in an area of interest. The PAS networks typically consist of public-supply wells that are relatively deep, the MAS networks typically consist of domestic-supply wells that are intermediate in depth, and the LUS networks typically consist of monitoring wells that are relatively shallow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203010","collaboration":"U.S. Geological Survey National Water-Quality Assessment","usgsCitation":"Ging, P.B., 2020, Water-quality comparison of the Gulf Coast aquifer system at various scales in Texas from National Water-Quality Assessment groundwater studies, 2013–15: U.S. Geological Survey Fact Sheet 2020–3010, 4 p., https://doi.org/10.3133/fs20203010.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111987","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399200,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109733.htm"},{"id":372712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3010/fs20203010.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3010"},{"id":372711,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3010/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Gulf Coast aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.5,\n 25.8378\n ],\n [\n -93.5069,\n 25.8378\n ],\n [\n -93.5069,\n 31.333\n ],\n [\n -98.5,\n 31.333\n ],\n [\n -98.5,\n 25.8378\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Predicting the decadal evolution of barrier island systems is important for coastal managers who propose restoration or preservation alternatives aimed at increasing the resiliency of the island and its associated habitats or communities. Existing numerical models for simulating morphologic changes typically include either long-term (for example, longshore transport under quiescent conditions) or short-term (for example, storm-driven waves) processes, with limited capacity to predict the decadal time-scale that is often most relevant in coastal planning. As part of the Alabama Barrier Island Restoration Assessment, a methodology was developed to predict barrier island evolution on decadal time scales. The developed modeling scheme uses multiple models including (1) Delft3D; (2) the empirical dune growth model (EDGR); and (3) XBeach that run sequentially to simulate evolution of barrier island geomorphology. The model framework was developed and applied to hindcast the evolution of Dauphin Island, Alabama, between 2004 and 2015, and was assessed using lidar data over the same period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191139","usgsCitation":"Mickey, R.C., Long, J.W., Dalyander, P.S., Jenkins, R.L., III, Thompson, D.M., Passeri, D.L., and Plant, N.G., 2019, Development of a modeling framework for predicting decadal barrier island evolution: U.S. Geological Survey Open-File Report 2019–1139, 46 p., https://doi.org/10.3133/ofr20191139.","productDescription":"Report: vi, 46 p.; Data Release","ipdsId":"IP-111247","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399428,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109735.htm"},{"id":372441,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ALL6C","text":"USGS data release","linkHelpText":"Dauphin Island decadal hindcast model inputs and results"},{"id":372678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2019/1139/ofr20191139.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1139"},{"id":372308,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201001","text":"Open-File Report 2020-1001","linkHelpText":"- Application of Decadal Modeling Approach to Forecast Barrier Island Evolution, Dauphin Island, Alabama"},{"id":372306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2019/1139/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37677001953125,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.18905718468536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701