A decline in submerged aquatic vegetation (SAV) within Florida’s spring-fed thermal refuges raises questions about how these systems support winter foraging of Florida manatees Trichechus manatus latirostris. We analyzed telemetry data for 12 manatees over 7 yr to assess their use of Kings Bay, a winter refuge with diminished SAV. After accounting for the effect of water temperature, we hypothesized that the number of trips out of Kings Bay would increase and the time wintering manatees spent in Kings Bay would decrease. Trips out of and into Kings Bay were also compared to assess potential influences on exiting or entering. There were no detectable differences in the number of trips out of the bay or overall time manatees spent in Kings Bay across winters. The percentage of time water temperatures were below 20°C was the single best predictor of increased time spent in Kings Bay. Trips out of Kings Bay were more likely than trips into the bay to occur after 12:00 h and during a high but ebbing tide. Nine manatees tracked for longer than 75 d in winter spent 7 to 57% of their time in the Gulf of Mexico, and 3 of these manatees spent 7 to 65% of the winter >80 km from the mouth of Kings Bay. Results suggest the low amount of SAV in Kings Bay does not obviate its use by manatees, though there are likely tradeoffs for manatees regularly foraging elsewhere. Accounting for movements of Florida manatees through a network of habitats may improve management strategies and facilitate desirable conservation outcomes.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr00933","usgsCitation":"Littles, C.J., Bonde, R.K., Butler, S.M., Jacoby, C.A., Notestein, S.K., Reid, J.P., Slone, D.H., and Frazer, T.K., 2019, Coastal habitat change and marine megafauna behavior: Florida manatees encountering reduced food provisions in a prominent winter refuge: Endangered Species Research, v. 38, p. 29-43, https://doi.org/10.3354/esr00933.","productDescription":"15 p.","startPage":"29","endPage":"43","ipdsId":"IP-088011","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467834,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00933","text":"Publisher Index Page"},{"id":361825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.80258178710938,\n 28.5941685062326\n ],\n [\n -82.56912231445312,\n 28.5941685062326\n ],\n [\n -82.56912231445312,\n 29.039361975917828\n ],\n [\n -82.80258178710938,\n 29.039361975917828\n ],\n [\n -82.80258178710938,\n 28.5941685062326\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Littles, Chanda J.","contributorId":214014,"corporation":false,"usgs":false,"family":"Littles","given":"Chanda","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":758925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonde, Robert K. 0000-0001-9179-4376 rbonde@usgs.gov","orcid":"https://orcid.org/0000-0001-9179-4376","contributorId":2675,"corporation":false,"usgs":true,"family":"Bonde","given":"Robert","email":"rbonde@usgs.gov","middleInitial":"K.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":758924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butler, Susan M. 0000-0003-3676-9332 sbutler@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-9332","contributorId":195796,"corporation":false,"usgs":true,"family":"Butler","given":"Susan","email":"sbutler@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":758926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacoby, Charles A.","contributorId":214015,"corporation":false,"usgs":false,"family":"Jacoby","given":"Charles","email":"","middleInitial":"A.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":758927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Notestein, Sky K.","contributorId":214017,"corporation":false,"usgs":false,"family":"Notestein","given":"Sky","email":"","middleInitial":"K.","affiliations":[{"id":35620,"text":"Southwest Florida Water Management District","active":true,"usgs":false}],"preferred":false,"id":758931,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reid, James P. 0000-0002-8497-1132 jreid@usgs.gov","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":3460,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"jreid@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":758928,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Slone, Daniel H. 0000-0002-9903-9727 dslone@usgs.gov","orcid":"https://orcid.org/0000-0002-9903-9727","contributorId":205617,"corporation":false,"usgs":true,"family":"Slone","given":"Daniel","email":"dslone@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":758929,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frazer, Thomas K.","contributorId":214016,"corporation":false,"usgs":false,"family":"Frazer","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":758930,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70200357,"text":"sir20185141 - 2019 - Spatial distribution of nutrients, chloride, and suspended sediment concentrations and loads determined by using different sampling methods in a cross section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015","interactions":[],"lastModifiedDate":"2019-03-08T10:17:20","indexId":"sir20185141","displayToPublicDate":"2019-03-07T10:00:00","publicationYear":"2019","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":"2018-5141","displayTitle":"Spatial Distribution of Nutrients, Chloride, and Suspended Sediment Concentrations and Loads Determined by Using Different Sampling Methods in a Cross Section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015","title":"Spatial distribution of nutrients, chloride, and suspended sediment concentrations and loads determined by using different sampling methods in a cross section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015","docAbstract":"The Detroit River separates the United States and Canada as it flows from Lake St. Clair to Lake Erie. The Trenton Channel is a 13-kilometer-long branch of the Detroit River that flows to the west of Grosse Ile before rejoining the Detroit River near its mouth, just before the Detroit River flows into Lake Erie. The U.S. Environmental Protection Agency has listed both the Trenton Channel and Detroit River as Areas of Concern because of a list of Beneficial Use Impairments such as interrupted drinking-water services, loss of aquatic life, and reduced recreational use. Phosphorus loading from tributaries such as the Trenton Channel is one of the primary drivers of eutrophication in Lake Erie. The complex flow patterns and variable distribution of chemical constituents in the Trenton Channel make it difficult to accurately characterize the concentrations and loads of nutrients and other constituents conveyed through the channel to Lake Erie.
In order to better understand the Trenton Channel’s contributions of nutrients (total phosphorus, orthophosphate, total nitrogen, and ammonia), chloride, and suspended sediment to Lake Erie and evaluate differences in results obtained by using different sample methodologies, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and Environment Canada, completed 12 sampling campaigns on the Trenton Channel in Detroit, Michigan, from November 2014 through November 2015.
Acoustic Doppler current profiler (ADCP) techniques were used to characterize the distribution of velocity components within a cross section corresponding to a transect of the Trenton Channel at U.S. Geological Survey station 041686401 Trenton Channel of Detroit River at Grosse Ile, Mich. Three methods of collecting water-quality data at the same transect of the Trenton Channel were used: multiple-vertical depth-integrated (MVDI), fixed-point, and discrete samples. Horizontal and vertical variations in concentrations of nutrients, chloride, and suspended sediment were analyzed from discrete samples to better understand distributions of these constituents throughout the channel. Constituent loads were calculated by using individual sample concentrations and ADCP measurements for discharge made on the same day that the water-quality samples were collected. Constituent loads calculated from MVDI and fixed-point sampling methods were compared. The relation between MVDI and fixed-point samples helped quantify the differences between the sampling methods. Linear regression equations depicting the relation between concentrations measured by using MVDI and fixed-point samples were prepared.
ADCP data indicates that velocities throughout the sampled transect remain uniform except for one location around 200 meters from the west bank of the channel. Secondary flow vectors suggest the presence of counter-rotating helical flow cells, and these helical flow cells could affect the mixing of constituents in transport by preventing cross-channel mixing. Flow discharges throughout the sampling campaign showed small variations, although lower flow rates were observed in the early winter months than in the summer months. Discrete sampling methods results displayed both heterogeneity throughout the channel horizontally, representing limited horizontal mixing in the channel, and displayed homogeneity throughout vertical transects, indicating mixing vertically. Comparisons between MVDI and fixed-point methods found consistently higher concentrations were measured in MVDI samples compared to concentrations measured in fixed-point samples. To correct for this bias between MVDI and fixed-point sample results, simple linear-regression equations were developed for all major constituents to help estimate constituent concentrations from fixed-point samples equivalent to those measured by using MVDI sampling techniques. Instantaneous constituent loads were developed by using velocity and discharge data obtained from ADCPs and constituent concentrations obtained from MVDI and fixed-point samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185141","collaboration":"Prepared in cooperation with the United States Environmental Protection Agency and Environment and Climate Change Canada","usgsCitation":"Totten, A.R., and Duris, J.W., 2019, Spatial distribution of nutrients, chloride, and suspended sediment concentrations and loads determined by using different sampling methods in a cross section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015: U.S. Geological Survey Scientific Investigations Report 2018–5141, 25 p., https://doi.org/10.3133/sir20185141.","productDescription":"viii, 25 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091065","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5141/sir20185141.pdf","text":"Report","size":"4.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5141"},{"id":361789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5141/coverthb.jpg"}],"country":"Canada, United States","state":"Michigan","otherGeospatial":"Detroit River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.26263427734375,\n 41.97174336327968\n ],\n [\n -82.78884887695312,\n 41.97174336327968\n ],\n [\n -82.78884887695312,\n 42.40622065620649\n ],\n [\n -83.26263427734375,\n 42.40622065620649\n ],\n [\n -83.26263427734375,\n 41.97174336327968\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911
Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.
","language":"English","publisher":"MDPI","doi":"10.3390/cli7030037","usgsCitation":"Jarnevich, C.S., and Young, N.E., 2019, Not so normal normals: Species distribution model results are sensitive to choice of climate normals and model type: Climate, v. 7, no. 3, p. 1-15, https://doi.org/10.3390/cli7030037.","productDescription":"Article 37; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-073502","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/cli7030037","text":"Publisher Index Page"},{"id":361797,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":758818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Nicholas E.","contributorId":189060,"corporation":false,"usgs":false,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":758819,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202493,"text":"70202493 - 2019 - The area under the precision‐recall curve as a performance metric for rare binary events","interactions":[],"lastModifiedDate":"2019-06-18T10:36:48","indexId":"70202493","displayToPublicDate":"2019-03-06T11:16:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"The area under the precision‐recall curve as a performance metric for rare binary events","docAbstract":"Background
Information about contemporary fire regimes across the Sky Island mountain ranges of the Madrean Archipelago Ecoregion in the southwestern United States and northern Mexico can provide insight into how historical fire management and land use have influenced fire regimes, and can be used to guide fuels management, ecological restoration, and habitat conservation. To contribute to a better understanding of spatial and temporal patterns of fires in the region relative to environmental and anthropogenic influences, we augmented existing fire perimeter data for the US by mapping wildfires that occurred in the Mexican Sky Islands from 1985 to 2011.
Results
A total of 254 fires were identified across the region: 99 fires in Mexico (μ = 3901 ha, σ = 5066 ha) and 155 in the US (μ = 3808 ha, σ = 8368 ha). The Animas, Chiricahua, Huachuca-Patagonia, and Santa Catalina mountains in the US, and El Pinito in Mexico had the highest proportion of total area burned (>50%) relative to Sky Island size. Sky Islands adjacent to the border had the greatest number of fires, and many of these fires were large with complex shapes. Wildfire occurred more often in remote biomes, characterized by evergreen woodlands and conifer forests with cooler, wetter conditions. The five largest fires (>25 000 ha) all occurred during twenty-first century droughts (2002 to 2003 and 2011); four of these were in the US and one in Mexico. Overall, high variation in fire shape and size were observed in both wetter and drier years, contributing to landscape heterogeneity across the region.
Conclusions
Future research on regional fire patterns, including fire severity, will enhance opportunities for collaborative efforts between countries, improve knowledge about ecological patterns and processes in the borderlands, and support long-term planning and restoration efforts.
Management activities to restore endangered fish species, such as dam removals, fishway installations, and periodic turbine shutdowns, usually decrease hydropower generation capacities at dams. Quantitative analysis of the tradeoffs between energy production and fish population recovery related to dam decision-making is still lacking. In this study, an integrated hydropower generation and age-structured fish population model was developed using a system dynamics modeling method to assess basin-scale energy-fish tradeoffs under eight dam management scenarios. This model ran across 150 years on a daily time step, applied to five hydroelectric dams located in the main stem of the Penobscot River, Maine. We used alewife (Alosa pseudoharengus) to be representative of the local diadromous fish populations to link projected hydropower production with theoretical influences on migratory fish populations on the model river system. Our results show that while the five dams can produce around 427 GWh/year of energy, without fishway installations they would contribute to a 90% reduction in the alewife spawner abundance. The effectiveness of fishway installations is largely influenced by the size of reopened habitat areas and the actual passage rate of the fishways. Homing to natal habitat has an insignificant effect on the growth of the simulated spawner abundance. Operating turbine shutdowns during alewives' peak downstream migration periods, in addition to other dam management strategies, can effectively increase the spawner abundance by 480–550% while also preserving 65% of the hydropower generation capacity. These data demonstrate that in a river system where active hydropower dams operate, a combination of dam management strategies at the basin scale can best balance the tradeoff between energy production and the potential for migratory fish population recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.03.042","usgsCitation":"Song, C., O’Malley, A., Roy, S.G., Zydlewski, J.D., Barber, B.L., and Mo, W., 2019, Managing dams for energy and fish tradeoffs: What does a win-win solution take?: Science of the Total Environment, v. 669, p. 833-843, https://doi.org/10.1016/j.scitotenv.2019.03.042.","productDescription":"11 p.","startPage":"833","endPage":"843","ipdsId":"IP-105717","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467840,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.03.042","text":"Publisher Index Page"},{"id":389809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.7744140625,\n 44.53959000445632\n ],\n [\n -68.3184814453125,\n 44.69599298172069\n ],\n [\n -68.2635498046875,\n 44.883120442385646\n ],\n [\n -68.1317138671875,\n 45.20913363773731\n ],\n [\n -68.18115234375,\n 45.38301927899065\n ],\n [\n -68.40087890624999,\n 45.57175504130605\n ],\n [\n -68.54919433593749,\n 45.69850658738846\n ],\n [\n -68.97216796875,\n 45.67932023569538\n ],\n [\n -69.01611328125,\n 45.57944511437787\n ],\n [\n -69.14794921875,\n 45.17041997262664\n ],\n [\n -69.169921875,\n 44.94536144236941\n ],\n [\n -69.41162109375,\n 44.68818283842486\n ],\n [\n -69.12597656249999,\n 44.453388800301774\n ],\n [\n -68.9117431640625,\n 44.469071224701096\n ],\n [\n -68.7744140625,\n 44.52392653654213\n ],\n [\n -68.7744140625,\n 44.53959000445632\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"669","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Cuihong","contributorId":265998,"corporation":false,"usgs":false,"family":"Song","given":"Cuihong","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":824000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Malley, Andrew","contributorId":169716,"corporation":false,"usgs":false,"family":"O’Malley","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":824001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Samuel G.","contributorId":266000,"corporation":false,"usgs":false,"family":"Roy","given":"Samuel","email":"","middleInitial":"G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":824002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":824003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barber, Betsy L.","contributorId":207173,"corporation":false,"usgs":false,"family":"Barber","given":"Betsy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":824004,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mo, Weiwei","contributorId":266002,"corporation":false,"usgs":false,"family":"Mo","given":"Weiwei","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":824005,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202336,"text":"sir20185166 - 2019 - Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016","interactions":[],"lastModifiedDate":"2019-03-06T14:01:08","indexId":"sir20185166","displayToPublicDate":"2019-03-06T07:46:29","publicationYear":"2019","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":"2018-5166","displayTitle":"Spatial and Temporal Variability of Harmful Algal Blooms in Milford Lake, Kansas, May through November 2016","title":"Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016","docAbstract":"The U.S. Geological Survey, in cooperation with the Kansas Department of Health and Environment (KDHE), completed a study to quantify the spatial and temporal variability of cyanobacterial blooms in Milford Lake, Kansas, over a range of environmental conditions at various time scales (hours to months). A better understanding of the spatial and temporal variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with cyanobacterial harmful algal bloom (CyanoHAB) issues throughout the Nation. Spatial and temporal variability were assessed in the upstream one-third of Milford Lake (designated as “Zone C” by KDHE) during May through November 2016 using a combination of time-lapse photography, continuous water-quality monitors, discrete phytoplankton, chlorophyll, and microcystin samples, and spatially dense near-surface data. Combined, these data were used to characterize variability of cyanobacterial abundance, algal biomass, and microcystin concentrations in Zone C of Milford Lake before, during, and after cyanobacterial blooms in 2016.
Temporal patterns were evaluated during May through November 2016 using time-lapse photography at six locations in Zone C and at a single point location (the Wakefield site) using a combination of discrete and continuously measured water-quality data (including the cyanobacterial pigment phycocyanin). Based on time-lapse photography, CyanoHABs developed in Zone C of Milford Lake in early July and persisted through the end of November. Bloom accumulations at individual sites were dependent on wind direction. After a change in wind direction, it would take about 1 day for accumulations to become visible at different locations. During periods with low wind, accumulations were widespread and visible at all sites. Cyanobacteria were absent from the algal community at the Wakefield site in late May and were a minor component of the community in June; however, by mid-July the cyanobacteria were dominant and remained dominant until early November.
Chlorophyll and microcystin concentrations at the Wakefield site were estimated using sensor-measured phycocyanin based on regression models developed for Zone C. Regression-estimated concentrations likely are more indicative of seasonal patterns in algal biomass (as indicated by chlorophyll concentrations) and microcystin than discretely collected samples because regression-estimated data have a much higher temporal resolution. Based on regression estimates, algal biomass and microcystin concentrations at the Wakefield site steadily increased from May through August. After August, concentrations decreased but remained relatively high compared to May and June. Daily chlorophyll maxima were as much as 400 times higher than daily minima, and daily microcystin maxima were as many as several orders of magnitude higher than daily minima. The extreme variability in algal biomass and microcystin concentrations at the Wakefield site reflects the development and dissipation of blooms, as indicated by the time-lapse cameras.
Based on regression-estimated microcystin concentrations, the KDHE watch and warning thresholds for microcystin were exceeded during mid-June through late November. Exceedance of KDHE advisory thresholds often changed from no advisory to watch or warning over the course of the day because of the variability in algal biomass and microcystin concentrations caused by bloom development and dissipation. Continuous water-quality monitors may be useful in informing public-health decisions in lakes with variable CyanoHAB conditions; however, site-specific models need to be developed, and best practices for using continuous water-quality monitors to inform CyanoHAB management strategies need to be established.
Spatial data were collected on May 26, July 21, and September 15, 2016, using a combination of a boat-mounted array and discrete water-quality samples analyzed for phytoplankton community composition and chlorophyll and microcystin concentrations. Spatial patterns were described using regression-estimated chlorophyll and microcystin concentrations. During the May 26, 2016, spatial surveys, cyanobacterial abundances were relatively low throughout Zone C and did not exceed KDHE guidance values compared to spatial surveys on July 21 and September 15. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass uplake in Zone C, and decreases in the downlake direction towards Zone B. Regression-estimated chlorophyll concentrations also were more variable uplake than downlake. Based on regression estimates, microcystin concentrations did not exceed KDHE guidance values anywhere in Zone C on May 26. Spatial patterns in microcystin throughout Zone C did not match patterns in regression-estimated chlorophyll concentrations, likely because the algal community was not dominated by cyanobacteria at most locations in May.
During the July 21, 2016, spatial surveys, cyanobacterial abundances in Zone C exceeded KDHE guidance values in 50 percent of samples. The algal community in Zone C was dominated by cyanobacteria at all locations except two, where cyanobacteria codominated with diatoms. Both locations where cyanobacteria and diatoms codominated were north of the causeway. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass north of the causeway and on the eastern shore of Zone C. On July 21, algal biomass did not always decrease in the downlake direction. There was a decrease just south of the causeway but an increase shortly after with higher concentrations into Zone B. Spatial maps indicated changes in algal distribution at a 0.5-meter depth, with algae moving to the central part of the lake north of the causeway and along the eastern shore south of the causeway. Most regression-estimated microcystin concentrations on July 21 exceeded KDHE guidance values, reflecting the pervasive bloom conditions in Zone C during this period. Spatial patterns in regression-estimated microcystin concentrations throughout Zone C were similar to patterns seen in discrete samples and regression-estimated chlorophyll concentrations, with higher concentrations north of the causeway and on the east shore of Zone C.
During the September 15, 2016, spatial surveys, cyanobacterial abundances did not exceed KDHE guidance values. The algal community north of the causeway was dominated by diatoms. The algal community throughout the rest of Zone C was dominated by cyanobacteria. Of regression-estimated microcystin concentrations on September 15, 80 percent did not exceed KDHE guidance values. Spatial patterns indicated northward movement of the cyanobacterial bloom consistent with a wind shift noted the previous day. On September 14, winds were generally from the north to northwest, shifting to the south by September 15. There was a northward progression of chlorophyll and microcystin during the spatial surveys. These data, along with the camera data and spatial and wind data from May and July, indicate that wind can be a major driver of the spatial and temporal variability of cyanobacterial blooms in Milford Lake and likely plays a role in the extent and duration of near-shore accumulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185166","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment","usgsCitation":"Foster, G.M., Graham, J.L., and King, L.R., 2019, Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016: U.S. Geological Survey Scientific Investigations Report 2018–5166, 36 p., https://doi.org/10.3133/sir20185166.","productDescription":"Report: vi, 36 p.; Appendixes: 28 p.; Data Releases: 4","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-093516","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":361764,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78S4P4M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality data from two sites on Milford Lake, Kansas, May 25–26, June 8–10, July 20–21, and September 14–15, 2016"},{"id":361765,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KCV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Time-lapse photography of Milford Lake, Kansas, June through November 2016"},{"id":361760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5166/coverthb.jpg"},{"id":361763,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5DVX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Milford Lake, Kansas spatial water-quality data, May 26, June 9, July 14, July 21, and September 15, 2016"},{"id":361761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166.PDF","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166"},{"id":361762,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166_appendixes.pdf","text":"Appendix 1 and 2","size":"571 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166 Appendixes 1 and 2"},{"id":361766,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7513XFN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, May through November 2016"}],"country":"United States","state":"Kansas","otherGeospatial":"Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 38.982897808179985\n ]\n ]\n ]\n }\n }\n ]\n}\n\n\n\n","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Peat cores are valuable archives of past environmental change because they accumulate plant organic matter over millennia. While studies have primarily focused on physical, ecological, and some biogeochemical proxies, cores from peatlands have increasingly been used to interpret hydroclimatic change using stable isotope analyses of cellulose preserved in plant remains. Previous studies indicate that the stable oxygen isotope compositions (δ18O) preserved in alpha cellulose extracted from specific plant macrofossils reflect the δ18O values of past peatland water and thereby provide information on long-term changes in hydrology in response to climate. Oxygen isotope analyses of peat cellulose (δ18Ocellulose) have been successfully developed from peat cores that accumulate the same species for millennia. However, to fully exploit the potential of this proxy in species-diverse fens, studies are needed that account for the isotopic variations caused by changes in dominant species composition. This study assesses variation in δ18O values among peatland plant species and how they relate to environmental waters in two fens informally named Horse Trail and Goldfin, located on the leeward (dry) and windward (wet) side, respectively, of the climatic gradient across the Kenai Peninsula, Alaska. Environmental water δ18O values at both fens reflect unmodified δ18O values of mean annual precipitation, although at Goldfin standing pools were slightly influenced by evaporation. Modern plant [mosses and Carex spp. (sedges)] δ18Ocellulose values indicate that all Carex spp. are higher (~2.5‰) than those of mosses, likely driven by their vascular structure and ecophysiological difference from non-vascular mosses. Moss δ18Ocellulose values within each peatland are similar among the species, and differences appear related to evaporation effects on environmental waters within hummocks and hollows. The plant taxa-environmental water δ18O differences are applied to the previously determined Horse Trail Fen untreated bulk δ18O record. Results include significant changes to inferred millennial-to-centennial scale hydroclimatic trends where dominant taxa shift from moss to Carex spp., indicating that modern calibration datasets are necessary for interpreting stable isotopes from fens, containing a mix of vascular and nonvascular plants. Accounting for isotopic offsets through macrofossil analysis and modern plant-water isotope measurements opens new opportunities for hydroclimatic reconstructions from fen peatlands.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2019.00025","usgsCitation":"Jones, M., Anderson, L., Keller, K., Nash, B., Littell, V., Wooller, M.J., and Jolley, C., 2019, An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions: Frontiers in Earth Science, v. 7, 25, 16 p., https://doi.org/10.3389/feart.2019.00025.","productDescription":"25, 16 p.","ipdsId":"IP-102651","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00025","text":"Publisher Index Page"},{"id":368887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arc Lake, Bear Lake, Bear Mountain Lake, Browse Lake, Headquarters Lake, Horse Trail clearing, Kenai Lake, Lower Ohmer Lake, Portage Lake, Skilak Lake, Summit Lake, Tern Lake, Upper Ohmer Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.578369140625,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 59.9274956808828\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -173.485107421875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.10319489936693\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Miriam 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":201994,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":774422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Lesleigh 0000-0002-5264-089X land@usgs.gov","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":436,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","email":"land@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":774423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Katherine 0000-0001-6915-5455","orcid":"https://orcid.org/0000-0001-6915-5455","contributorId":218048,"corporation":false,"usgs":false,"family":"Keller","given":"Katherine","email":"","affiliations":[{"id":39732,"text":"Natural Systems Analysts, Harvard University","active":true,"usgs":false}],"preferred":false,"id":774424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nash, Bailey 0000-0001-6423-2773 bnash@usgs.gov","orcid":"https://orcid.org/0000-0001-6423-2773","contributorId":220192,"corporation":false,"usgs":true,"family":"Nash","given":"Bailey","email":"bnash@usgs.gov","affiliations":[{"id":40146,"text":"Iowa State University, Ames, IA","active":true,"usgs":false},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":774425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Virginia","contributorId":220193,"corporation":false,"usgs":false,"family":"Littell","given":"Virginia","email":"","affiliations":[{"id":40147,"text":"University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":774426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jolley, Chelsea","contributorId":220194,"corporation":false,"usgs":false,"family":"Jolley","given":"Chelsea","email":"","affiliations":[{"id":26916,"text":"Brigham Young University, Provo, UT","active":true,"usgs":false}],"preferred":false,"id":774428,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205300,"text":"70205300 - 2019 - Hormones and pharmaceuticals in groundwater used as a source of drinking water across the United States","interactions":[],"lastModifiedDate":"2019-09-13T15:11:37","indexId":"70205300","displayToPublicDate":"2019-03-05T10:44:53","publicationYear":"2019","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":"Hormones and pharmaceuticals in groundwater used as a source of drinking water across the United States","docAbstract":"This is the first large-scale, systematic assessment of hormone and pharmaceutical occurrence in groundwater used for drinking across the United States. Samples from 1091 sites in Principal Aquifers representing 60% of the volume pumped for drinking-water supply had final data for 21 hormones and 103 pharmaceuticals. At least one compound was detected at 5.9% of 844 sites representing the resource used for public supply across the entirety of 15 Principal Aquifers, and at 11.3% of 247 sites representing the resource used for domestic supply over subareas of nine Principal Aquifers. Of 34 compounds detected, one plastics component (bisphenol A), three pharmaceuticals (carbamazepine, sulfamethoxazole, and meprobamate), and the caffeine degradate 1,7-dimethylxanthine were detected in more than 0.5% of samples. Hydrocortisone had a concentration greater than a human-health benchmark at 1 site. Compounds with high solubility and low Koc were most likely to be detected. Detections were most common in shallow wells with a component of recent recharge, particularly in crystalline-rock and mixed land-use settings. Results indicate vulnerability of groundwater used for drinking water in the U.S. to contamination by these compounds is generally limited, and exposure to these compounds at detected concentrations is unlikely to have adverse effects on human health.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.8b05592","usgsCitation":"Bexfield, L.M., Toccalino, P., Belitz, K., Foreman, W.T., and Furlong, E., 2019, Hormones and pharmaceuticals in groundwater used as a source of drinking water across the United States: Environmental Science & Technology, v. 53, no. 6, p. 2950-2960, https://doi.org/10.1021/acs.est.8b05592.","productDescription":"Article: 11 p.; 3 Data Releases ","startPage":"2950","endPage":"2960","ipdsId":"IP-076014","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"links":[{"id":460449,"rank":5,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.8b05592","text":"Publisher Index Page"},{"id":367404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367409,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OM9PFB","text":"USGS data release","description":"USGS data release","linkHelpText":"Environmental and Quality-Control Data Collected by the USGS National Water-Quality Assessment Project for Hormones and Pharmaceuticals in Groundwater Used as a Source of Drinking Water Across the United States, 2013-15"},{"id":367407,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92D26LI","text":"USGS data release","description":"USGS data release","linkHelpText":"Third-party performance assessment data encompassing the time period of analysis of groundwater samples collected for hormones and pharmaceuticals by the National Water-Quality Assessment Project in 2013-15"},{"id":367408,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CL7K3F","text":"USGS data release","description":"USGS data release","linkHelpText":"Laboratory Quality-Control Data Associated with Groundwater Samples Collected for Hormones and Pharmaceuticals by the National Water-Quality Assessment Project in 2013-15"}],"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":"53","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toccalino, Patricia 0000-0003-1066-1702","orcid":"https://orcid.org/0000-0003-1066-1702","contributorId":213736,"corporation":false,"usgs":true,"family":"Toccalino","given":"Patricia","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":770811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":770812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foreman, William T. 0000-0002-2530-3310 wforeman@usgs.gov","orcid":"https://orcid.org/0000-0002-2530-3310","contributorId":190786,"corporation":false,"usgs":true,"family":"Foreman","given":"William","email":"wforeman@usgs.gov","middleInitial":"T.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":770813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":770814,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215498,"text":"70215498 - 2019 - Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","interactions":[],"lastModifiedDate":"2020-10-21T15:39:49.079734","indexId":"70215498","displayToPublicDate":"2019-03-05T10:36:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","docAbstract":"Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.
Background
Spatio-temporal patterns of movement can characterize relationships between organisms and their surroundings, and address gaps in our understanding of species ecology, activity budgets, bioenergetics, and habitat resource management. Highly mobile waterfowl, which can exploit resources over large spatial extents, are excellent models to understand relationships between movements and resource usage, landscape interactions and specific habitat needs.
Methods
We tracked 3 species of dabbling ducks with GPS-GSM transmitters in 2015–17 to examine fine-scale movement patterns over 24 h periods (30 min interval), dividing movement pathways into temporally continuous segments and spatially contiguous patches. We quantified distances moved, area used and time allocated across the day, using linear and generalized linear mixed models. We investigated behavior through relationships between these variables.
Results
Movements and space-use were small, and varied by species, sex and season. Gadwall (Mareca strepera) generally moved least (FFDs: 0.5–0.7 km), but their larger foraging patches resulted from longer within-area movements. Pintails (Anas acuta) moved most, were more likely to conduct flights > 300 m, had FFDs of 0.8–1.1 km, used more segments and patches per day that they revisited more frequently, resulting in the longest daily total movements. Females and males differed only during the post-hunt season when females moved more. 23.6% of track segments were short duration (1–2 locations), approximately 1/3 more than would be expected if they occurred randomly, and were more dispersed in the landscape than longer segments. Distance moved in 30 min shortened as segment duration increased, likely reflecting phases of non-movement captured within segments.
Conclusions
Pacific Flyway ducks spend the majority of time using smaller foraging and resting areas than expected or previously reported, implying that foraging areas may be highly localized, and nutrients obtainable from smaller areas. Additionally, movement reductions over time demonstrates behavioral adjustments that represent divergent energetic demands, the detection of which is a key advantage of higher frequency data. Ducks likely use less energy for movement than currently predicted and management, including distribution and configuration of essential habitat, may require reconsideration. Our study illustrates how fine-scale movement data from tracking help understand and inform various other fields of research.
The Vilkitskii Basin is a separate petroleum province that lies beneath the continental shelf of the East Siberian Sea east of the New Siberian Islands and northwest of Wrangel Island. It is a basin known only on the basis of gravity data and three seismic profiles. A small, southern part of the basin overlies the Brooks Range–Chukotka late Mesozoic-early Paleogene orogenic belt, but most of the basin lies north of that belt. Its regional setting suggests that it may have similarities to other post-orogenic (successor) basins on the East Siberian Shelf as well as to foreland, rift-sag, and passive margin basins lying north of the orogenic belt such as the North Slope, North Chukchi and Podvodnikov Basins.
Although the basin’s petroleum potential is poorly known, extremely thick sediments, diapiric structures, and gas plumes interpreted from a seismic profile are considered favorable features for petroleum presence and imply that there may be an active petroleum system. An overall probability of about 30 percent of at least one petroleum accumulation >50 MMBOE (million barrels of oil equivalent) was determined based on estimated probabilities of the occurrence of petroleum source, adequate reservoir, trap and seal, and favorable timing. A single assessment unit (AU) was defined and assessed, resulting in mean estimates of undiscovered, technically recoverable resources that include about 100 million barrels of oil and 5,500 billion cubic feet of nonassociated gas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824Z","usgsCitation":"Bird, K.J., Houseknecht, D.W., and Pitman, J.K., 2019, Geology and assessment of undiscovered oil and gas resources of the Vilkitskii Basin Province, 2008, chap. Z of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 12 p., https://doi.org/10.3133/pp1824Z.","productDescription":"Document: vi, 11 p.; Larger Work; Appendix","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-050995","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":361711,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/z/pp1824z_appendix1.xls","text":"Appendix 1","size":"39 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824Z Appendix 1","linkHelpText":"Input Data for the Vilkitskii Basin Assessment Unit"},{"id":361710,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/z/pp1824z.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824Z"},{"id":361709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/z/coverthb.jpg"}],"otherGeospatial":"Vilkitskii Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 160,\n 72\n ],\n [\n 178,\n 72\n ],\n [\n 178,\n 79\n ],\n [\n 160,\n 79\n ],\n [\n 160,\n 72\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
In June, 2017, we acquired seismic data along five linear profiles at three British Columbia Hydro and Power Authority (BC Hydro, a Canadian provincial Crown Corporation) dam sites (John Hart, Ladore, and Strathcona Dams) on Vancouver Island, British Columbia, Canada. We also attempted to acquire linear seismic profiles at two additional BC Hydro dam sites (Ruskin Dam and Stave Falls Dam) east of the City of Vancouver, British Columbia, Canada; however, due to a seismograph programming error, little active-source data from Ruskin Dam and Stave Falls Dam were recorded. Thus, results from Ruskin Dam and Stave Falls Dam are not included in this report. At the three dam sites with successful data acquisition, we acquired both active- and passive-source data. Data acquisition details for each of the three dam sites varied in terms of seismic sources, the number of seismographs, and profile length and orientation. However, for active-source acquisition at each dam site, we acquired one or more linear seismic profiles ranging in length from about 150 to 400 meters (m), and along each profile, seismograph spacing was either 3 m or 5 m (see appendix 1). All data were recorded in three components (vertical and two horizontals). To greatly increase the resolution of the seismic velocity structure along these profiles, we co-located active sources at each seismograph.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191015","usgsCitation":"Catchings, R.D., Addo, K.O., Goldman, M.R., Chan, J.H., Sickler, R.R., and Criley, C.J., 2019, Two-dimensional seismic velocities and structural variations at three British Columbia Hydro and Power Authority (BC Hydro) dam sites, Vancouver Island, British Columbia, Canada: U.S. Geological Survey Open-File Report 2019–1015, 125 p., https://doi.org/10.3133/ofr20191015.","productDescription":"Report: ix, 125 p.; Data release","numberOfPages":"137","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099439","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":361737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1015/coverthb.jpg"},{"id":361738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1015/ofr20191015.pdf","text":"Report","size":"32.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1015"},{"id":361739,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PRGZ53","text":"USGS data release","description":"USGS Data Release","linkHelpText":"2017 U.S. Geological Survey/BC Hydro seismic data recorded at three dam sites on Vancouver Island, British Columbia, Canada"}],"country":"Canada","state":"British Columbia","otherGeospatial":"Vancouver Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.6036718615,\n 49.90411690637\n ],\n [\n -125.30053124888,\n 49.90411690637\n ],\n [\n -125.30053124888,\n 50.100507966958\n ],\n [\n -125.6036718615,\n 50.100507966958\n ],\n [\n -125.6036718615,\n 49.90411690637\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center — Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
This study used a 20-year dataset (1995–2014) to identify factors affecting cyanobacterial community composition (CCC) and abundance in a eutrophic lake. We hypothesized that differences in thermal structure, nutrients, and meteorology drive interannual variability in CCC and abundance. Cluster analysis differentiated dominant cyanobacteria into rare, low abundance, or sporadically occurring taxa. The bloom-forming genera were Microcystis and Aphanizomenon, accounting for ~ 70% of total cyanobacterial biovolume (BV) on average, whereas unusually high abundance of Planktothrix, Synechococcus, and Oscillatoria were clear outliers in three of the years. Variability in CCC was significantly correlated (P < 0.05, R > 0.3) with ice duration, Kjeldahl nitrogen (TKN), and spring nitrite + nitrate (NO2+3); ice duration and TKN were associated with the occurrence of primarily non-bloom-forming genera. Pairwise correlations tested linear, exponential, and polynomial correlates of absolute and relative total Cyanophyta, Microcystis, or Aphanizomenon BV. TKN, total nitrogen (TN) and phosphorus (TP), TN:TP ratio, Schmidt stability, and rainfall correlated with total Cyanophyta, Microcystis, and Aphanizomenon BV, whereas ice cover, NO2+3, and TKN correlated with relative Microcystis and Aphanizomenon BV. Despite increasing TN:TP ratio over two decades, cyanobacterial abundance had not changed significantly. These data suggest differing responses of cyanobacterial genera to important environmental factors over two decades.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-018-3810-x","usgsCitation":"Weirich, C.A., Robertson, D.M., and Miller, T.R., 2019, Physical, biogeochemical, and meteorological factors responsible for interannual changes in cyanobacterial community composition and biovolume over two decades in a eutrophic lake: Hydrobiologia, v. 828, no. 1, p. 165-182, https://doi.org/10.1007/s10750-018-3810-x.","productDescription":"18 p.","startPage":"165","endPage":"182","ipdsId":"IP-095911","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"828","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Weirich, Chelsea A. 0000-0002-2481-4987","orcid":"https://orcid.org/0000-0002-2481-4987","contributorId":213923,"corporation":false,"usgs":false,"family":"Weirich","given":"Chelsea","email":"","middleInitial":"A.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":758700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Todd R. 0000-0002-2113-1662","orcid":"https://orcid.org/0000-0002-2113-1662","contributorId":213924,"corporation":false,"usgs":false,"family":"Miller","given":"Todd","email":"","middleInitial":"R.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":758701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202464,"text":"70202464 - 2019 - Isotopic and petrologic investigation, and a thermomechanical model of genesis of large-volume rhyolites in arc environments: Karymshina Volcanic Complex, Kamchatka, Russia","interactions":[],"lastModifiedDate":"2019-08-15T11:51:18","indexId":"70202464","displayToPublicDate":"2019-03-04T15:25:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic and petrologic investigation, and a thermomechanical model of genesis of large-volume rhyolites in arc environments: Karymshina Volcanic Complex, Kamchatka, Russia","docAbstract":"The Kamchatka Peninsula of eastern Russia is currently one of the most volcanically active areas on Earth where a combination of >8 cm/yr subduction convergence rate and thick continental crust generates large silicic magma chambers, reflected by abundant large calderas and caldera complexes. This study examines the largest center of silicic 4-0.5 Ma Karymshina Volcanic Complex, which includes the 25 × 15 km Karymshina caldera, the largest in Kamchatka. A series of rhyolitic tuff eruptions at 4 Ma were followed by the main eruption at 1.78 Ma and produced an estimated 800 km3 of rhyolitic ignimbrites followed by high-silica rhyolitic post-caldera extrusions. The postcaldera domes trace the 1.78 Ma right fracture and form a continuous compositional series with ignimbrites. We here present results of a geologic, petrologic, and isotopic study of the Karymshina eruptive complex, and present new Ar-Ar ages, and isotopic values of rocks for the oldest pre- 1.78 Ma caldera ignimbrites and intrusions, which include a diversity of compositions from basalts to rhyolites. Temporal trends in δ18O, 87Sr/86Sr, and 144Nd/143Nd indicate values comparable to neighboring volcanoes, increase in homogeneity, and temporal increase in mantle-derived Sr and Nd with increasing differentiation over the last 4 million years. Data are consistent with a batholithic scale magma chamber formed by primarily fractional crystallization of mantle derived composition and assimilation of Cretaceous and younger crust, driven by basaltic volcanism and mantle delaminations. All rocks have 35–45% quartz, plagioclase, biotite, and amphibole phenocrysts. Rhyolite-MELTS crystallization models favor shallow (2 kbar) differentiation conditions and varying quantities of assimilated amphibolite partial melt and hydrothermally-altered silicic rock. Thermomechanical modeling with a typical 0.001 km3/yr eruption rate of hydrous basalt into a 38 km Kamchatkan arc crust produces two magma bodies, one near the Moho and the other engulfing the entire section of upper crust. Rising basalts are trapped in the lower portion of an upper crustal magma body, which exists in a partially molten to solid state. Differentiation products of basalt periodically mix with the resident magma diluting its crustal isotopic signatures. At the end of the magmatism crust is thickened by 8 km. Thermomechanical modeling show that the most likely way to generate large spikes of rhyolitic magmatism is through delamination of cumulates and mantle lithosphere after many millions of years of crustal thickening. The paper also presents a chemical dataset for Pacific ashes from ODDP 882 and 883 and compares them to Karymshina ignimbrites and two other Pleistocene calderas studied by us in earlier works.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2018.00238","usgsCitation":"Bindeman, I.N., Leonov, V.L., Colon, D.P., Rogozin, A.N., Shipley, N., Jicha, B., Loewen, M.W., and Gerya, T.V., 2019, Isotopic and petrologic investigation, and a thermomechanical model of genesis of large-volume rhyolites in arc environments: Karymshina Volcanic Complex, Kamchatka, Russia: Frontiers in Earth Science, v. 6, 238; 27 p., https://doi.org/10.3389/feart.2018.00238.","productDescription":"238; 27 p.","ipdsId":"IP-102469","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00238","text":"Publisher Index Page"},{"id":361712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Kamchatka","volume":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Bindeman, Ilya N.","contributorId":175500,"corporation":false,"usgs":false,"family":"Bindeman","given":"Ilya","email":"","middleInitial":"N.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":758692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonov, Vladimir L.","contributorId":213917,"corporation":false,"usgs":false,"family":"Leonov","given":"Vladimir","email":"","middleInitial":"L.","affiliations":[{"id":38929,"text":"Institute of Volcanology and Seismology","active":true,"usgs":false}],"preferred":false,"id":758693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colon, Dylan P.","contributorId":213918,"corporation":false,"usgs":false,"family":"Colon","given":"Dylan","email":"","middleInitial":"P.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":758694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogozin, Aleksey N.","contributorId":213919,"corporation":false,"usgs":false,"family":"Rogozin","given":"Aleksey","email":"","middleInitial":"N.","affiliations":[{"id":38929,"text":"Institute of Volcanology and Seismology","active":true,"usgs":false}],"preferred":false,"id":758695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shipley, Niccole","contributorId":213921,"corporation":false,"usgs":false,"family":"Shipley","given":"Niccole","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":758697,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jicha, Brian","contributorId":213920,"corporation":false,"usgs":false,"family":"Jicha","given":"Brian","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":758696,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loewen, Matthew W. 0000-0002-5621-285X","orcid":"https://orcid.org/0000-0002-5621-285X","contributorId":213321,"corporation":false,"usgs":true,"family":"Loewen","given":"Matthew","email":"","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":758691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gerya, Taras V.","contributorId":213922,"corporation":false,"usgs":false,"family":"Gerya","given":"Taras","email":"","middleInitial":"V.","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":758698,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70215596,"text":"70215596 - 2019 - Unprocessed atmospheric nitrate in waters of the Northern Forest Region in the USA and Canada","interactions":[],"lastModifiedDate":"2020-10-25T18:19:51.970621","indexId":"70215596","displayToPublicDate":"2019-03-04T13:16:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Unprocessed atmospheric nitrate in waters of the Northern Forest Region in the USA and Canada","docAbstract":"Little is known about the regional extent and variability of nitrate from atmospheric deposition that is transported to streams without biological processing in forests. We measured water chemistry and isotopic tracers (δ18O and δ15N) of nitrate sources across the Northern Forest Region of the U.S. and Canada and reanalyzed data from other studies to determine when, where, and how unprocessed atmospheric nitrate was transported in catchments. These inputs were more widespread and numerous than commonly recognized, but with high spatial and temporal variability. Only 6 of 32 streams had high fractions (>20%) of unprocessed atmospheric nitrate during baseflow. Seventeen had high fractions during stormflow or snowmelt, which corresponded to large fractions in near-surface soil waters or groundwaters, but not deep groundwater. The remaining 10 streams occasionally had some (<20%) unprocessed atmospheric nitrate during stormflow or baseflow. Large, sporadic events may continue to be cryptic due to atmospheric deposition variation among storms and a near complete lack of monitoring for these events. A general lack of observance may bias perceptions of occurrence; sustained monitoring of chronic nitrogen pollution effects on forests with nitrate source apportionments may offer insights needed to advance the science as well as assess regulatory and management schemes.
The National Ecological Observatory Network (NEON) is designed to facilitate an understanding of the impact of environmental change on ecological systems. Observations of plant diversity—responsive to changes in climate, disturbance, and land use, and ecologically linked to soil, biogeochemistry, and organisms—result in NEON data products that cross a range of organizational levels. Collections include samples of plant tissue to enable investigations of genetics, plot‐based observations of incidence and cover of native and non‐native species, observations of plant functional traits, archived vouchers of plants, and remote sensing airborne observations. Spatially integrating many ecological observations allows a description of the relationship of plant diversity to climate, land use, organisms, and substrates. Repeating the observations over decades and across the United States will iteratively improve our understanding of those relationships and allow for the testing of system‐level hypotheses as well as the development of predictions of future conditions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2603","usgsCitation":"Barnett, D.T., Adler, P.B., Chemel, B.R., Duffy, P.A., Enquist, B.J., Grace, J.B., Harrison, S.P., Peet, R.K., Schimel, D.S., Stohlgren, T.J., and Vellend, M., 2019, The plant diversity sampling design for The National Ecological Observatory Network: Ecosphere, v. 10, no. 2, p. 1-18, https://doi.org/10.1002/ecs2.2603.","productDescription":"e02603; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-093102","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467851,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2603","text":"Publisher Index Page"},{"id":361675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnett, David T.","contributorId":213893,"corporation":false,"usgs":false,"family":"Barnett","given":"David","email":"","middleInitial":"T.","affiliations":[{"id":38923,"text":"National Ecological Observatory Network (NEON), Inc.","active":true,"usgs":false}],"preferred":false,"id":758627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adler, Peter B.","contributorId":64789,"corporation":false,"usgs":false,"family":"Adler","given":"Peter","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":758628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chemel, Benjamin R.","contributorId":213894,"corporation":false,"usgs":false,"family":"Chemel","given":"Benjamin","email":"","middleInitial":"R.","affiliations":[{"id":38924,"text":"Northern Rockies Conservation Cooperative","active":true,"usgs":false}],"preferred":false,"id":758629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duffy, Paul A.","contributorId":148013,"corporation":false,"usgs":false,"family":"Duffy","given":"Paul","email":"","middleInitial":"A.","affiliations":[{"id":16973,"text":"Neptune and Company Inc.","active":true,"usgs":false}],"preferred":false,"id":758630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Enquist, Brian J.","contributorId":177416,"corporation":false,"usgs":false,"family":"Enquist","given":"Brian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":758631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":758626,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harrison, Susan P.","contributorId":147735,"corporation":false,"usgs":false,"family":"Harrison","given":"Susan","email":"","middleInitial":"P.","affiliations":[{"id":16917,"text":"Dept. of Env. Sci. and Policy, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":758632,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peet, Robert K.","contributorId":12711,"corporation":false,"usgs":false,"family":"Peet","given":"Robert","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":758633,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schimel, David S.","contributorId":211508,"corporation":false,"usgs":false,"family":"Schimel","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":758634,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stohlgren, Thomas J.","contributorId":213895,"corporation":false,"usgs":false,"family":"Stohlgren","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":38925,"text":"Natural Resource Ecology Laboratory, Colorado State University, Fort Collins","active":true,"usgs":false}],"preferred":false,"id":758635,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vellend, Mark","contributorId":213896,"corporation":false,"usgs":false,"family":"Vellend","given":"Mark","email":"","affiliations":[{"id":38926,"text":"De´partement de biologie, Universite´ de Sherbrooke, Quebec, Canada","active":true,"usgs":false}],"preferred":false,"id":758636,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202455,"text":"70202455 - 2019 - Heat and mass transport in a vapor-dominated hydrothermal area in Yellowstone National Park, USA: Inferences from magnetic, electrical, electromagnetic, subsurface temperature and diffuse CO2 flux measurements","interactions":[],"lastModifiedDate":"2019-03-04T10:29:35","indexId":"70202455","displayToPublicDate":"2019-03-04T10:29:31","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Heat and mass transport in a vapor-dominated hydrothermal area in Yellowstone National Park, USA: Inferences from magnetic, electrical, electromagnetic, subsurface temperature and diffuse CO2 flux measurements","title":"Heat and mass transport in a vapor-dominated hydrothermal area in Yellowstone National Park, USA: Inferences from magnetic, electrical, electromagnetic, subsurface temperature and diffuse CO2 flux measurements","docAbstract":"Vapor‐dominated hydrothermal systems are characterized by localized and elevated heat and gas flux. In these systems, steam and gas ascend from a boiling water reservoir, steam condenses beneath a low‐permeability cap layer, and liquid water descends, driven by gravity (“heat pipe” model). We combine magnetic, electromagnetic, and geoelectrical methods and CO2 flux and subsurface temperature measurements in the Solfatara Plateau Thermal Area in the Yellowstone Caldera to address several fundamental questions: (1) What are the structural and/or lithological controls on heat and mass transport in vapor‐dominated areas? (2) What is the geometry and size of convecting multiphase thermal plumes? (3) Are thermal plumes associated with subsurface rock alteration and demagnetization? Magnetic and electromagnetic data inversions suggest an asymmetric 50‐ to 100‐m thick basin of glacial deposits with the thickest part adjacent to the margin of a rhyolite flow. The 3‐D electrical conductivity model in the glacial basin reveals a narrow vertical conductor interpreted as a focused multiphase plume, which coincides at the ground surface with the heat and CO2 flux maxima. The magnetic data suggest that destruction of magnetic minerals due to rock alteration associated with the hydrothermal plume occurs mainly near the ground surface. We propose a model where the buoyant multiphase plume forms in response to decompression, boiling, and phase separation of pressurized thermal groundwater that discharges from the brecciated base of a rhyolite flow into the basin of glacial deposits. Results from multiphase groundwater flow and heat transport numerical simulations corroborate the first‐order characteristics of this model.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB016202","usgsCitation":"Bouligand, C., Hurwitz, S., Vandemeulebrouck, J., Byrdina, S., Kass, M.A., and Lewicki, J.L., 2019, Heat and mass transport in a vapor-dominated hydrothermal area in Yellowstone National Park, USA: Inferences from magnetic, electrical, electromagnetic, subsurface temperature and diffuse CO2 flux measurements: Journal of Geophysical Research B: Solid Earth, v. 124, no. 1, p. 291-309, https://doi.org/10.1029/2018JB016202.","productDescription":"19 p.","startPage":"291","endPage":"309","ipdsId":"IP-098703","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488797,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/e8f358ca-154d-4c4f-a21a-cd87db837f8c","text":"External Repository"},{"id":361674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.75,\n 44.5833\n ],\n [\n -110.5,\n 44.5833\n ],\n [\n -110.5,\n 44.75\n ],\n [\n -110.75,\n 44.75\n ],\n [\n -110.75,\n 44.5833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bouligand, Claire","contributorId":71662,"corporation":false,"usgs":true,"family":"Bouligand","given":"Claire","affiliations":[],"preferred":false,"id":758657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":758658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandemeulebrouck, Jean","contributorId":101973,"corporation":false,"usgs":true,"family":"Vandemeulebrouck","given":"Jean","email":"","affiliations":[],"preferred":false,"id":758659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Byrdina, Svetlana","contributorId":213911,"corporation":false,"usgs":false,"family":"Byrdina","given":"Svetlana","email":"","affiliations":[],"preferred":false,"id":758660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kass, Mason A. 0000-0001-6119-2593 mkass@usgs.gov","orcid":"https://orcid.org/0000-0001-6119-2593","contributorId":613,"corporation":false,"usgs":true,"family":"Kass","given":"Mason","email":"mkass@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":758661,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":758662,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201030,"text":"sir20185161 - 2019 - Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate","interactions":[],"lastModifiedDate":"2019-03-06T09:26:09","indexId":"sir20185161","displayToPublicDate":"2019-03-04T10:11:16","publicationYear":"2019","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":"2018-5161","displayTitle":"Assessment of Columbia and Willamette River Flood Stage on the Columbia Corridor Levee System at Portland, Oregon, in a Future Climate","title":"Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate","docAbstract":"To support Levee Ready Columbia’s (LRC’s) effort to re-certify levees along the Columbia and Willamette Rivers and remain accredited, two 2-dimensional hydraulic models, Adaptive Hydraulics and Delft3D-Flexible Mesh, were used to simulate the effects of plausible extreme high water during the 2030 to 2059 period. The Columbia River was simulated from Bonneville Dam, situated at river mile (RM) 145, to the mouth of Columbia River, and the Willamette River was simulated from Willamette Falls, RM 26.2, to the Columbia River confluence. Inputs to the models included light detection and ranging (lidar) and bathymetric mapping data to determine bed level, and boundary conditions in the form of daily inflow hydrographs and water levels in the ocean offshore of the mouth of the Columbia River.
Future conditions were based on climate science data developed by the U.S. Army Corps of Engineers and others. These conditions included future streamflow and coastal ocean water levels. The hypothetical, extreme but plausible, upstream boundary was based on scaling up the hydrographs from the 1996 flood. Scaling factors were determined by comparing the peak flow rankings determined from flood frequency analyses of historical unregulated periods and 2040s simulated unregulated winter streamflow. The comparison resulted in scaling up the Columbia River hydrograph by 40-percent and scaling up the Willamette River and Lower Columbia River tributaries hydrographs by 20-percent. The downstream ocean boundary was based on a combination of sea-level change, high tide, and storm surge.
The models were calibrated for two historical periods: (1) from January 15 to February 28, 1996, and (2) from April 12 to July 12, 1997. The two models compared well to the measured water-surface elevation over the historical periods and had good performance statistics, with root-mean square error ranging from 0.085 to 0.32 meters, Nash-Sutcliffe values greater than 0.96, and bias ranging from -0.03 to 0.28 meters. The simulated peak stage in the Columbia River at Vancouver, Washington, for 1996 was 9.60 and 9.98 meters (31.5 and 32.7 feet) compared to the measured peak of 9.89 meters (32.5 feet). Future peak stage then was simulated with boundary conditions representing extreme but plausible future conditions at the inflow sites and the ocean boundary.
The two calibrated models compared well in their simulations of extreme but plausible future conditions. For the 0-meter sea-level change scenario, the simulated peak stage in the Columbia River at Vancouver was 11.15 and 11.39 meters (36.6 and 37.4 feet); and for the 1-meter sea-level change scenario, the simulated peak stage in the Columbia River was 11.25 and 11.54 meters (36.9 and 37.9 feet). The total increase in stage as compared to the 1996 measured peak stage ranged from 1.26 to 1.65 meters (4.13 to 5.40 feet).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185161","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Levee Ready Columbia","usgsCitation":"Wherry, S.A., Wood, T.M., Moritz, H.R., and Duffy, K.B., 2019, Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate: U.S. Geological Survey Scientific Investigations Report 2018-5161, 44 p., https://doi.org/10.3133/sir20185161.","productDescription":"vii, 44 p.","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-096367","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":361538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5161/coverthb.jpg"},{"id":361539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5161/sir20185161.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5161"}],"country":"United States","state":"Oregon","city":"Portland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.112548828125,\n 44.953136827528816\n ],\n [\n -119.9981689453125,\n 44.953136827528816\n ],\n [\n -119.9981689453125,\n 46.5172957536981\n ],\n [\n -124.112548828125,\n 46.5172957536981\n ],\n [\n -124.112548828125,\n 44.953136827528816\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The Arizona hedgehog cactus (Echinocereus triglochidiatus var. arizonicus) is endemic to central Arizona in Gila and Pinal Counties, and has been federally listed as endangered by the U.S. Fish and Wildlife Service (FWS) since 1979. Mining, mineral exploration, and highway development have resulted in habitat degradation and loss of individual plants. Therefore, decreases in the population of the cactus are expected to continue. In response to a request from FWS to compile, evaluate, and synthesize data for the cactus, we identified and evaluated existing survey and monitoring data for the cactus and conducted a demographic analysis with suitable data.
Systematic surveys for the Arizona hedgehog cactus did not begin until the late 1970s. Early surveys generally were anecdotal descriptions of cactus populations and precisely georeferenced records of individual cactus occurrence did not occur until global positioning systems were widely used. Much of the georeferenced data have been collected by consultants for mining operations, the Arizona Department of Transportation, the U.S. Forest Service, and independent surveyors. Occurrence records have been compiled by the Arizona Game and Fish Department Heritage Data Management System, but submission of these data may be incomplete, and the attributes reported have varied among the contributing entities. The compilation and management of survey data is essential for field-based evidence of the size, distribution, and range extent of the cactus. In support of consistency in future survey data collection, this report makes several suggestions for future surveys.
Monitoring for the Arizona hedgehog cactus, defined as repeat observations of the status of cactus individuals, has been done by consulting companies for three mines. Demographic monitoring further involves marking individual cacti in consistently defined plots and recording the fate of each cacti through time, including birth, growth, reproduction, and death. We were able to use demographic monitoring data provided by two consulting companies to calculate survival and population growth rates, using several statistical approaches. Resulting models indicate that larger cacti, as measured by their number of stems, have greater survival rates. Larger individuals also had higher probability of producing more flowers. Small cacti had the lowest survivorship, with potentially only 15–20 percent reaching large size. Most populations monitored by the two companies were stable to increasing. However, there were differences in the growth rates among plots and some plots had negative population growth rates. The demographic monitoring data we used represented relatively dense populations of undisturbed cacti. Hence, overall positive population growth rates were not influenced by any large-scale disturbances. Previous analyses with cacti and other species suggest that more than 10 years of data are necessary to accurately forecast long-term population trajectories. As the monitoring intervals we evaluated were shorter, they represent short-term dynamics only. Several suggestions are made in the report to improve collection of monitoring data to support evidence-based estimates of demographic characteristics of the Arizona hedgehog cactus.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191004","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service, Arizona Ecological Services","usgsCitation":"Thomas, K.A., Shryock, D.F., and Esque, T.C., 2019, Arizona hedgehog cactus (Echinocereus triglochidiatus var. arizonicus)—A systematic data assessment in support of recovery: U.S. Geological Survey Open-File Report 2019-1004, 36 p., https://doi.org/10.3133/ofr20191004.","productDescription":"viii, 36 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-099658","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":361617,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1004/ofr20191004.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1004"},{"id":361616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1004/coverthb.jpg"}],"country":"United States","state":"Arizona","county":"Gila County, Pinal County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.22421264648438,\n 33.25534082823907\n ],\n [\n -110.9014892578125,\n 33.25534082823907\n ],\n [\n -110.9014892578125,\n 33.48070852506531\n ],\n [\n -111.22421264648438,\n 33.48070852506531\n ],\n [\n -111.22421264648438,\n 33.25534082823907\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Southwest Biological Science Center
520 N. Park Avenue, Suite 221
University of Arizona, Building 120
Tucson, Arizona 85719
Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these “partial” observations must be modified to include the missing data mechanism to avoid spurious inference.
We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data.
We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk (Cervus elaphus nelsoni) to demonstrate improved inference for the proportions of sex and stage classes.
We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.