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":70202492,"text":"70202492 - 2019 - Patterns of mercury and selenium exposure in Minnesota common loons","interactions":[],"lastModifiedDate":"2019-03-06T11:18:40","indexId":"70202492","displayToPublicDate":"2019-03-06T11:18:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of mercury and selenium exposure in Minnesota common loons","docAbstract":"Common loons (Gavia immer) are at risk of elevated dietary mercury (Hg) exposure in portions of their breeding range. To assess the level of risk among loons in Minnesota (USA), we investigated loon blood Hg concentrations in breeding lakes across Minnesota. Loon blood Hg concentrations were regressed on predicted Hg concentrations in standardized 12‐cm whole‐organism yellow perch (Perca flavescens), based on fish Hg records from Minnesota lakes, using the US Geological Survey National Descriptive Model for Mercury in Fish. A linear model, incorporating common loon sex, age, body mass, and log‐transformed standardized perch Hg concentration representative of each study lake, was associated with 83% of the variability in observed common loon blood Hg concentrations. Loon blood Hg concentration was positively related to standardized perch Hg concentrations; juvenile loons had lower blood Hg concentrations than adult females, and blood Hg concentrations of juveniles increased with body mass. Blood Hg concentrations of all adult common loons and associated standardized prey Hg for all loon capture lakes included in the study were well below proposed thresholds for adverse effects on loon behavior, physiology, survival, and reproductive success. The fish Hg modeling approach provided insights into spatial patterns of dietary Hg exposure risk to common loons across Minnesota. We also determined that loon blood selenium (Se) concentrations were positively correlated with Hg concentration. Average common loon blood Se concentrations exceeded the published provisional threshold.
","language":"English","publisher":"Society for Environmental Toxicology and Chemistry (SETAC)","doi":"10.1002/etc.4331","usgsCitation":"Kenow, K.P., Houdek, S.C., Fara, L., Erickson, R.A., Gray, B.R., Harrison, T.J., Monson, B., and Henderson, C.L., 2019, Patterns of mercury and selenium exposure in Minnesota common loons: Environmental Toxicology and Chemistry, v. 38, no. 3, p. 524-532, https://doi.org/10.1002/etc.4331.","productDescription":"9 p.","startPage":"524","endPage":"532","ipdsId":"IP-099038","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437550,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QHT2DG","text":"USGS data release","linkHelpText":"Code to analyze loon blood mercury"},{"id":437549,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96XLGLP","text":"USGS data release","linkHelpText":"Loon mercury models"},{"id":437548,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TDCH3F","text":"USGS data release","linkHelpText":"Patterns of mercury and selenium exposure in Minnesota common loons: Data"},{"id":361796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenow, Kevin P. 0000-0002-3062-5197 kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houdek, Steven C. 0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fara, Luke J. 0000-0002-1143-4395","orcid":"https://orcid.org/0000-0002-1143-4395","contributorId":202973,"corporation":false,"usgs":true,"family":"Fara","given":"Luke J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758825,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758827,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758828,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monson, Bruce 0000-0002-3632-2875","orcid":"https://orcid.org/0000-0002-3632-2875","contributorId":211998,"corporation":false,"usgs":false,"family":"Monson","given":"Bruce","email":"","affiliations":[{"id":13330,"text":"Minnesota Pollution Control Agency","active":true,"usgs":false}],"preferred":false,"id":758829,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henderson, Carrol L.","contributorId":213967,"corporation":false,"usgs":false,"family":"Henderson","given":"Carrol","email":"","middleInitial":"L.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":758830,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"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":"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":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","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
The chaparral-dominated national forests of southern California were in part established to provide water provision services to the surrounding urban populations and irrigation for agriculture. However, water provision in the form of groundwater recharge and surface runoff depends on the climatological conditions of any given year and also landscape-scale disturbances such as fire. Fire is increasing in frequency in southern California and understanding its impacts both immediately postfire and as vegetation recovers, and the interactions between fire and hydrology, are key components to managing federal lands effectively. In this study we focus on nine fires in a study area that encompasses the four southern California national forests (Los Padres, Angeles, San Bernardino, and Cleveland) and use a water balance model to investigate the effects of water provision services post-fire at a regional scale. We found that runoff and recharge increased post-fire, with increases in recharge being greater with recovery times ranging from 2 to 4 y post-fire. Vegetation recovery occurred 2 y post-fire for all basins as indicated by remotely sensed imagery measuring vegetation greenness having returned to or exceeded pre-fire values for the basin. We found that runoff and recharge were more sensitive to the effects of climate than to length of time post-fire. Findings from these modeling tools allow users to anticipate the impact of fire on water provision services in the region and develop management strategies that help reduce the impacts of wildfire.
In the U.S., about 44 million people rely on self-supplied groundwater for drinking water. Because most self-supplied homeowners do not treat their water to control corrosion, drinking water can be susceptible to lead (Pb) contamination from metal plumbing. To assess the types and locations of susceptible groundwater, a geochemical reaction model that included pure Pb minerals and solid solutions of calcite (CaxPb1–xCO3) and apatite [CaxPb5-x(PO4)3(OH; Cl; F)] was developed to estimate the lead solubility potential (LSP) for over 8300 untreated groundwater samples collected from domestic and public-supply sites between 2000 and 2016 in the U.S. The LSP is the calculated amount of Pb metal that could dissolve at 25 °C before a Pb-bearing mineral precipitates. About 33% of untreated groundwater samples had LSP greater than 15 μg/L—the USEPA action level for dissolved plus particulate forms of Pb. Five percent of samples had high LSP (above 300 μg/L) and tended to occur in the eastern and southeastern U.S. Measured Pb concentrations above 15 μg/L were rarely detected (<1%) but always coincided with high LSP values. Future work will provide a better understanding of the relation between water chemistry, Pb-mineral formation, and dissolved Pb concentrations in tap water.
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]\n}","volume":"53","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parkhurst, David L. 0000-0003-3348-1544 dlpark@usgs.gov","orcid":"https://orcid.org/0000-0003-3348-1544","contributorId":1088,"corporation":false,"usgs":true,"family":"Parkhurst","given":"David","email":"dlpark@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":770837,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":213728,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":770838,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206406,"text":"70206406 - 2019 - An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions","interactions":[],"lastModifiedDate":"2020-03-27T08:34:48","indexId":"70206406","displayToPublicDate":"2019-03-05T11:51:02","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":"An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions","docAbstract":"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":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 earthquake activity in Oklahoma and Kansas that began in 2008 reflects the most widespread instance of induced seismicity observed to date. We develop a reservoir model to calculate the hydrologic conditions associated with the activity of 902 saltwater disposal wells injecting into the Arbuckle aquifer. Estimates of basement fault stressing conditions inform a rate‐and‐state friction earthquake nucleation model to forecast the seismic response to injection. Our model replicates many salient features of the induced earthquake sequence, including the onset of seismicity, the timing of the peak seismicity rate, and the reduction in seismicity following decreased disposal activity. We present evidence for variable time lags between changes in injection and seismicity rates, consistent with the prediction from rate‐and‐state theory that seismicity rate transients occur over timescales inversely proportional to stressing rate. Given the efficacy of the hydromechanical model, as confirmed through a likelihood statistical test, the results of this study support broader integration of earthquake physics within seismic hazard analysis.
Catchment models are useful tools to help describe and quantify the sources, transport, and fate of sediment, nutrients, and other constituents in a landscape. Results from catchment models are used to quantify and understand existing conditions and used in restoration efforts by defining areas with highest contributions (hotspots, where actions would be most beneficial) and describing the relative importance of various sources (what types of actions would be most beneficial). In practice, a continuum of models exists from simple empirical models to complex process-driven models, each requiring different types and amounts of information. Each of these models has its strengths and weaknesses, which should be considered when deciding which model to apply to a specific area. In many applications, a combination of models can be either coupled or run in series to help describe how nutrients and sediment are transported from the field to downstream receiving water bodies. In this chapter, we describe the continuum of catchment models that exist and provide information for choosing specific models for various management applications. We then provide examples of catchment models used to address a wide range of scientific and policy driven issues: two models commonly applied in New Zealand (CLUES and GLEAMS) and one model (SPARROW) applied to a large river basin in the United States (Mississippi River Basin).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Lake restoration handbook","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-93043-5_2","usgsCitation":"Parshotam, A., and Robertson, D.M., 2019, Modelling for catchment management, chap. of Lake restoration handbook, p. 25-65, https://doi.org/10.1007/978-3-319-93043-5_2.","productDescription":"41 p.","startPage":"25","endPage":"65","ipdsId":"IP-074204","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-30","publicationStatus":"PW","contributors":{"editors":[{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":758761,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Collier, Kevin J.","contributorId":213943,"corporation":false,"usgs":false,"family":"Collier","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":758762,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Quinn, John M.","contributorId":47469,"corporation":false,"usgs":true,"family":"Quinn","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":758763,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Howard-Williams, Clive","contributorId":213944,"corporation":false,"usgs":false,"family":"Howard-Williams","given":"Clive","email":"","affiliations":[],"preferred":false,"id":758764,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Parshotam, Aroon","contributorId":213925,"corporation":false,"usgs":false,"family":"Parshotam","given":"Aroon","email":"","affiliations":[{"id":38930,"text":"Waikato University, New Zealand","active":true,"usgs":false}],"preferred":false,"id":758703,"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":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758702,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":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":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - 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Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","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.
The Cooperative Fish and Wildlife Research Units (CRU) program had an interesting and challenging year in 2018. We made significant strategic advances on many fronts and had setbacks in others.
Our relationship with the U.S. Fish and Wildlife Service, the agency we belonged to from 1935 to the mid-1990s, was further reinforced through strategic efforts with the Service’s Science Applications senior staff. This is bearing fruit in terms of research collaborations and funding support. As part of a larger effort between the U.S. Geological Survey (USGS) Ecosystems Mission Area and the Service’s endangered species program, we are also collaborating to address science needs for species in pre-listing status. Barry Grand, Unit Supervisor (South), has been instrumental in this effort.
Tom Edwards of the Utah Unit has met with representatives of the U.S. Fish and Wildlife Service and the Association of Fish and Wildlife Agencies to promote training of leaders and “hands dirty” biologists in species distribution modeling. The Association passed a unanimous resolution endorsing the training at their midyear meeting in March. Tom held a workshop at the annual meeting of the Association of Fish and Wildlife Agencies in September, and future workshops, supported by the U.S. Fish and Wildlife Service, will be held bringing State agency and U.S. Fish and Wildlife Service biologists together to work on species of common concern.
Wyoming Unit Leader Matt Kauffman’s pioneering work in identifying and mapping big-game migration corridors has captured the attention of conservationists far and wide. In the spring, the Secretary of the Interior signed Secretarial Order No. 3362, \"Improving Habitat Quality in Western Big-Game Winter Range and Migration Corridors\" directing efforts of several U.S. Department of the Interior (DOI) bureaus to collaborate with States in identifying and protecting big-game corridors in 11 States. Matt has conducted several workshops that directly support the Secretarial order, and more are planned. Corridor mapping efforts supported by the USGS and the DOI, based in the States and coordinated by Matt, are unfolding.
Unit Administrative Officer Shana Coulby and her staff hosted a training program for university support staff at USGS National Headquarters in March. Shana’s team did a superb job, and the camaraderie among all was evident.
We co-sponsored the third in a series of workshops at the North American Wildlife and Natural Resources Conference in March on bridging the gap between science and management.
The State Department requested that we coordinate a workshop that would bring CRU scientists and other U.S. representatives together with Brazilian, Colombian, and Peruvian scientists and decision makers to develop best practices to minimize environmental damage from infrastructure development in the Amazon and to collaborate on science needs. The workshop was held in Iquitos, Peru, in the heart of the Amazon during August.
Our cooperator community, represented by the National Cooperators Coalition, was very active in response to the President’s budget proposal that would have redirected funding for the CRU program to other priorities. Their efforts are reflected in the House and Senate marks on the fiscal year 2019 budget that not only restored funding, but recommended increases.
You will see in this report many other accomplishments of our individual scientists and students during 2018. It was an impressive and productive year! What you won’t see chronicled is the work of the CRU headquarters staff and University support staff. These folks are extraordinary in their dedication to working with cooperators and scientists to solve problems and ensure the important work gets accomplished with minimal interference. We are truly fortunate to have such skilled and dedicated folks in the trenches.
I was fortunate to visit several units during 2018. For me, this is the most enriching part of my duties. I get to see firsthand the work our scientists do, the incredible students being mentored, and meet our cooperators on their turf.
As we look forward towards the horizon, 2019 looks brighter for the CRU program. Efforts by our cooperators to generate support for filling our vacancies are materializing. Our cadre of scientists is second to none, and the breadth and depth of our work are nothing short of impressive. Thanks to all who are part of this cooperative endeavor—conservation is the ultimate winner in our efforts!
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1452","isbn":"978-1-4113-4287-3","collaboration":" ","usgsCitation":"Organ, J.F., Thompson, J.D., Childs, D.E., and Dennerline, D.E., 2019, Cooperative Fish and Wildlife Research Units program—2018 year in review (ver. 1.1, March 29, 2019): U.S. Geological Survey Circular 1452, 52 p., https://doi.org/10.3133/cir1452.","productDescription":"iii, 52 p.","numberOfPages":"60","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-098975","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":361572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1452/coverthb2.jpg"},{"id":361574,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/gip187","text":"General Information Product 187","linkHelpText":"- Cooperative Fish and Wildlife Research Units Program—2018 Year in Review (postcard)"},{"id":362533,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1452/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":361573,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1452/circ1452.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1452"}],"country":"United States","edition":"Version 1.0: March 1, 2019; Version 1.1: March 29,2019","contact":"Cooperative Fish and Wildlife Research Units Program
U.S. Geological Survey
Mail Stop 303
12201 Sunrise Valley Drive
Reston, VA 20192
Purpose of Review
We examined recent literature on promoting habitat connectivity in the context of climate change (CC) and land-use change (LUC). These two global change forcings have wide-reaching ecological effects that are projected to worsen in the future. Improving connectivity is a common adaptation strategy, but CC and LUC can also degrade planned connections, potentially reducing their effectiveness. We synthesize advances in connectivity design approaches, identify challenges confronted by researchers and practitioners, and offer suggestions for future research.
Recent Findings
Recent studies incorporated future CC into connectivity design more often than LUC and rarely considered the two drivers jointly. When considering CC, most studies have focused on relatively broad spatial and temporal extents and have included either species-based targets or coarse-filter targets like geodiversity and climate gradients. High levels of uncertainty about future LUC and lack of consistent, readily available model simulations are likely hindering its inclusion in connectivity modeling. This high degree of uncertainty extends to efforts to jointly consider future CC and LUC.
Summary
We argue that successful promotion of connectivity as a means to adapt to CC and LUC will depend on (1) the velocity of CC, (2) the velocity of LUC, and (3) the degree of existing landscape fragmentation. We present a new conceptual framework to assist in identifying connectivity networks given these three factors. Given the high uncertainty associated with future CC and LUC, incorporating insights from decision science into connectivity planning will facilitate the development of more robust adaptation strategies.
Dividing the Waters offers this groundwater science bench book that cannot be matched by any other scientific or judicial publication. Adjudicating Groundwater combines the expertise and experience of academic scientists (UC Davis/Stanford), federal scientists (U.S. Geological Survey), and judicial officers to create a resource that can fulfill the needs of judges tackling the most difficult groundwater conflicts. This bench book explains both the fundamentals of groundwater science (hydrogeology) and groundwater modeling. The bench book received peer review from scientists and judges, with oversight by the U.S. Geological Survey, making this book among the most reliable resources for judges with water cases.
","language":"English","publisher":"The National Judicial College","usgsCitation":"Harter, T., Moran, T., and Wildman, E., 2019, Adjudicating groundwater: A judge’s guide to understanding groundwater and modeling.","ipdsId":"IP-097084","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":362180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":362145,"type":{"id":15,"text":"Index Page"},"url":"https://www.judges.org/dtw/adjudicating-groundwater/"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Brandt, Alf","contributorId":214284,"corporation":false,"usgs":false,"family":"Brandt","given":"Alf","email":"","affiliations":[],"preferred":false,"id":759544,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759545,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759546,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Harter, Thomas","contributorId":178245,"corporation":false,"usgs":false,"family":"Harter","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":759547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moran, Tara","contributorId":214282,"corporation":false,"usgs":false,"family":"Moran","given":"Tara","email":"","affiliations":[],"preferred":false,"id":759548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wildman, Eric","contributorId":214283,"corporation":false,"usgs":false,"family":"Wildman","given":"Eric","email":"","affiliations":[],"preferred":false,"id":759549,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}