A comprehensive inventory of ecosystem services across the entire Great Lakes basin is currently lacking and is needed to make informed management decisions. A greater appreciation and understanding of ecosystem services, including both use and non-use services, may have avoided misguided resource management decisions in the past that resulted in negative legacies inherited by future generations. Given the interest in ecosystem services and lack of a coherent approach to addressing this topic in the Great Lakes, a summit was convened involving 28 experts working on various aspects of ecosystem services in the Great Lakes. The invited attendees spanned a variety of social and natural sciences. Given the unique status of the Great Lakes as the world's largest collective repository of surface freshwater, and the numerous stressors threatening this valuable resource, timing was propitious to examine ecosystem services. Several themes and recommendations emerged from the summit. There was general consensus that: 1) a comprehensive inventory of ecosystem services throughout the Great Lakes is a desirable goal but would require considerable resources; 2) more spatially and temporally intensive data are needed to overcome our data gaps, but the arrangement of data networks and observatories must be well-coordinated; 3) trade-offs must be considered as part of ecosystem services analyses; and 4) formation of a Great Lakes Institute for Ecosystem Services, to provide a hub for research, meetings, and training is desirable. Several challenges also emerged during the summit, which are discussed.
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results","docAbstract":"Since passage of the Clean Water Act in 1972, Federal, State, and local governments have invested billions of dollars to reduce pollution entering rivers and streams. To understand the return on these investments and to effectively manage and protect the Nation’s water resources in the future, we need to know how and why water quality has been changing over time. As part of the National Water-Quality Assessment Project, of the U.S. Geological Survey’s National Water-Quality Program, data from the U.S. Geological Survey, along with multiple other Federal, State, Tribal, regional, and local agencies, have been used to support the most comprehensive assessment conducted to date of surface-water-quality trends in the United States. This report documents the methods used to determine trends in water quality and ecology because these methods are vital to ensuring the quality of the results. Specific objectives are to document (1) the data compilation and processing steps used to identify river and stream sites throughout the Nation suitable for water-quality, pesticide, and ecology trend analysis, (2) the statistical methods used to determine trends in target parameters, (3) considerations for water-quality, pesticide, and ecology data and streamflow data when modeling trends, (4) sensitivity analyses for selecting data and interpreting trend results with the Weighted Regressions on Time, Discharge, and Season method, and (5) the final trend results at each site. The scope of this study includes trends in water-quality concentrations and loads (nutrient, sediment, major ion, salinity, and carbon), pesticide concentrations and loads, and metrics for aquatic ecology (fish, invertebrates, and algae) for four time periods: (1) 1972–2012, (2) 1982–2012, (3) 1992–2012, and (4) 2002–12. In total, nearly 12,000 trends in concentration, load, and ecology metrics were evaluated in this study; there were 11,893 combinations of sites, parameters, and trend periods. The final trend results are presented with examples of how to interpret the results from each trend model. Interpretation of the trend results, such as causal analysis, is not included.
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States\"}}]}","edition":"Version 1.0: Originally posted April 4, 2017; Version 2.0: November 1, 2017","contact":"Program Coordinator, National Water Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
Plumes of buoyant river water spread in the ocean from river mouths, and these plumes influence water quality, sediment dispersal, primary productivity, and circulation along the world’s coasts. Most investigations of river plumes have focused on large rivers in a coastal region, for which the physical spreading of the plume is assumed to be independent from the influence of other buoyant plumes. Here we provide new understanding of the spreading patterns of multiple plumes interacting along simplified coastal settings by investigating: (i) the relative likelihood of plume-to-plume interactions at different settings using geophysical scaling, (ii) the diversity of plume frontal collision types and the effects of these collisions on spreading patterns of plume waters using a two-dimensional hydrodynamic model, and (iii) the fundamental differences in plume spreading patterns between coasts with single and multiple rivers using a three-dimensional hydrodynamic model. Geophysical scaling suggests that coastal margins with numerous small rivers (watershed areas < 10,000 km2), such as found along most active geologic coastal margins, were much more likely to have river plumes that collide and interact than coastal settings with large rivers (watershed areas > 100,000 km2). When two plume fronts meet, several types of collision attributes were found, including refection, subduction and occlusion. We found that the relative differences in pre-collision plume densities and thicknesses strongly influenced the resulting collision types. The three-dimensional spreading of buoyant plumes was found to be influenced by the presence of additional rivers for all modeled scenarios, including those with and without Coriolis and wind. Combined, these results suggest that plume-to-plume interactions are common phenomena for coastal regions offshore of the world’s smaller rivers and for coastal settings with multiple river mouths in close proximity, and that the spreading and fate of river waters in these settings will be strongly influenced by these interactions. We conclude that new investigations are needed to characterize how plumes interact offshore of river mouths to better understand the transport and fate of terrestrial sources of pollution, nutrients and other materials in the ocean.
The U.S. Geological Survey (USGS), as part of the National Assessment of Storm-Induced Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms. On June 9, 2011, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a Beechcraft BE90 King Air (aircraft) at an altitude of 500 feet (ft) (152 meters (m)) and approximately 1,200 ft (366 m) offshore. This mission was conducted to collect baseline data for assessing incremental changes in the beach and nearshore area and can be used to assess future coastal change.
The photographs in this report are Joint Photographic Experts Group (JPEG) images. These photographs document the state of the barrier islands and other coastal features at the time of the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1044","usgsCitation":"Morgan, K.L.M., 2017, Baseline coastal oblique aerial photographs collected from Dauphin Island, Alabama, to Breton Island, Louisiana, June 9, 2011: U.S. Geological Survey Data Series 1044, https://doi.org/10.3133/ds1044.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077797","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1044/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"}},{"id":338168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1044/coverthb.jpg"}],"country":"United States","state":"Alabama, Louisiana","otherGeospatial":"Breton Island, Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.16778564453125,\n 29.602118211647333\n ],\n [\n -88.05816650390625,\n 29.602118211647333\n ],\n [\n -88.05816650390625,\n 30.28041626667403\n ],\n [\n -89.16778564453125,\n 30.28041626667403\n ],\n [\n -89.16778564453125,\n 29.602118211647333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
The U.S. Geological Survey (USGS), as part of the National Assessment of Storm-Induced Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms. On August 31, 2005, the USGS conducted an oblique aerial photographic survey from Panama City, Florida, to Lakeshore, Mississippi, and the Chandeleur Islands, Louisiana, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet and approximately 1,000 feet offshore. This mission was flown to collect post-Hurricane Katrina data, which can be used to assess incremental changes in the beach and nearshore area and can be used to assess future coastal change.
The photographs in this report are Joint Photographic Experts Group (JPEG) images. These photographs document the state of the barrier islands and other coastal features at the time of the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1033","usgsCitation":"Morgan, K.L.M., and DeWitt, N.T., 2017, Post-Hurricane Katrina coastal oblique aerial photographs collected from Panama City, Florida, to Lakeshore, Mississippi, and the Chandeleur Islands, Louisiana, August 31, 2005: U.S. Geological Survey Data Series 1033, https://doi.org/10.3133/ds1033.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079893","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1033/coverthb.jpg"},{"id":338245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1033/index.html","text":"Report HTML"}],"country":"United States","state":"Florida","city":"Panama City","otherGeospatial":"Pensacola Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.20672607421875,\n 30.337324394179017\n ],\n [\n -87.19024658203124,\n 30.30176068632071\n ],\n [\n -86.87713623046875,\n 30.36102635890718\n ],\n [\n -86.59423828125,\n 30.372875188118016\n ],\n [\n -86.3140869140625,\n 30.351546261929034\n ],\n [\n -86.143798828125,\n 30.29701788337205\n ],\n [\n -85.94879150390625,\n 30.225848323247707\n ],\n [\n -85.7537841796875,\n 30.107117887092357\n ],\n [\n -85.6988525390625,\n 30.1380015549519\n ],\n [\n -85.98724365234375,\n 30.28990324883237\n ],\n [\n -86.2591552734375,\n 30.372875188118016\n ],\n [\n -86.46514892578124,\n 30.401306519203583\n ],\n [\n -86.77276611328125,\n 30.413150465068853\n ],\n [\n -86.96502685546875,\n 30.391830328088137\n ],\n [\n -87.20672607421875,\n 30.337324394179017\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
The importance of trophic linkages between aquatic and terrestrial ecosystems is predicted to vary as a function of subsidy quantity and quality relative to in situ resources. To test this prediction, I used multi-year diet data from Bonneville cutthroat trout Oncorhynchus clarki Utah in spring-fed and snowmelt-driven streams in the high desert of western North America. I documented that trout in spring-fed streams consumed more (number and weight) aquatic than terrestrial invertebrates, while trout in snowmelt-driven streams consumed a similar number of both prey types but consumed more terrestrial than aquatic invertebrates by weight. Trout in spring-fed streams consumed more aquatic invertebrates than trout in snowmelt streams and trout consumed more terrestrial invertebrates in snowmelt than in spring-fed streams. Up to 93% of trout production in spring-fed streams and 60% in snowmelt streams was fueled by aquatic invertebrates, while the remainder of trout production in each stream type was from terrestrial production. I found that the biomass and occurrence of consumed terrestrial invertebrates were not related to our measures of in situ resource quality or quantity in either stream type. These empirical data highlight the importance of autotrophic-derived production to trout in xeric regions.
","language":"English","publisher":"Oikos Publishers","publisherLocation":"La Crosse, WI","doi":"10.1080/02705060.2017.1284696","usgsCitation":"Sepulveda, A.J., 2017, Terrestrial–aquatic linkages in spring-fed and snowmelt-dominated streams: Journal of Freshwater Ecology, v. 32, no. 1, p. 288-299, https://doi.org/10.1080/02705060.2017.1284696.","productDescription":"12 p.","startPage":"288","endPage":"299","ipdsId":"IP-079530","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469951,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2017.1284696","text":"Publisher Index Page"},{"id":339034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Bear River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.81816101074217,\n 42.30626081896345\n ],\n [\n -111.35879516601561,\n 42.30626081896345\n ],\n [\n -111.35879516601561,\n 42.66022161324799\n ],\n [\n -111.81816101074217,\n 42.66022161324799\n ],\n [\n -111.81816101074217,\n 42.30626081896345\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-15","publicationStatus":"PW","scienceBaseUri":"58e35f7ee4b09da67997eca5","contributors":{"authors":[{"text":"Sepulveda, Adam J. 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":150628,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":687968,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70199835,"text":"70199835 - 2017 - An integrated population model for bird monitoring in North America","interactions":[],"lastModifiedDate":"2018-10-01T14:34:33","indexId":"70199835","displayToPublicDate":"2017-04-01T14:34:23","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"An integrated population model for bird monitoring in North America","docAbstract":"Integrated population models (IPMs) provide a unified framework for simultaneously analyzing data sets of different types to estimate vital rates, population size, and dynamics; assess contributions of demographic parameters to population changes; and assess population viability. Strengths of an IPM include the ability to estimate latent parameters and improve the precision of parameter estimates. We present a hierarchical IPM that combines two broad‐scale avian monitoring data sets: count data from the North American Breeding Bird Survey (BBS) and capture–recapture data from the Monitoring Avian Productivity and Survivorship (MAPS) program. These data sets are characterized by large numbers of sample sites and observers, factors capable of inducing error in the sampling and observation processes. The IPM integrates the data sets by modeling the population abundance as a first‐order autoregressive function of the previous year's population abundance and vital rates. BBS counts were modeled as a log‐linear function of the annual index of population abundance, observation effects (observer identity and first survey year), and overdispersion. Vital rates modeled included adult apparent survival, estimated from a transient Cormack‐Jolly‐Seber model using MAPS data, and recruitment (surviving hatched birds from the previous season + dispersing adults) estimated as a latent parameter. An assessment of the IPM demonstrated it could recover true parameter values from 200 simulated data sets. The IPM was applied to data sets (1992–2008) of two bird species, Gray Catbird (Dumetella carolinensis) and Wood Thrush (Hylocichla mustelina) in the New England/Mid‐Atlantic coastal Bird Conservation Region of the United States. The Gray Catbird population was relatively stable (trend +0.4% per yr), while the Wood Thrush population nearly halved (trend −4.5% per yr) over the 17‐yr study period. IPM estimates of population growth rates, adult survival, and detection and residency probabilities were similar and as precise as estimates from the stand‐alone BBS and CJS models. A benefit of using the IPM was its ability to estimate the latent recruitment parameter. Annual growth rates for both species correlated more with recruitment than survival, and the relationship for Wood Thrush was stronger than for Gray Catbird. The IPM's unified modeling framework facilitates integration of these important data sets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1493","usgsCitation":"Ahrestani, F.S., Saracco, J.F., Sauer, J.R., Pardieck, K.L., and Royle, J.A., 2017, An integrated population model for bird monitoring in North America: Ecological Applications, v. 27, no. 3, p. 916-924, https://doi.org/10.1002/eap.1493.","productDescription":"9 p.","startPage":"916","endPage":"924","ipdsId":"IP-080990","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":357969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-21","publicationStatus":"PW","scienceBaseUri":"5bc031aae4b0fc368eb53a40","contributors":{"authors":[{"text":"Ahrestani, Farshid S.","contributorId":208349,"corporation":false,"usgs":false,"family":"Ahrestani","given":"Farshid","email":"","middleInitial":"S.","affiliations":[{"id":37785,"text":"The Institute of Bird Populations","active":true,"usgs":false}],"preferred":false,"id":746840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saracco, James F.","contributorId":208350,"corporation":false,"usgs":false,"family":"Saracco","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":37785,"text":"The Institute of Bird Populations","active":true,"usgs":false}],"preferred":false,"id":746841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746843,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70181997,"text":"70181997 - 2017 - Trends in snowmelt-related streamflow timing in the conterminous United States","interactions":[],"lastModifiedDate":"2018-08-07T14:33:22","indexId":"70181997","displayToPublicDate":"2017-04-01T14:33:14","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Trends in snowmelt-related streamflow timing in the conterminous United States","docAbstract":"Changes in snowmelt-related streamflow timing have implications for water availability and use as well as ecologically relevant shifts in streamflow. Historical trends in snowmelt-related streamflow timing (winter-spring center volume date, WSCVD) were computed for minimally disturbed river basins in the conterminous United States. WSCVD was computed by summing daily streamflow for a seasonal window then calculating the day that half of the seasonal volume had flowed past the gage. We used basins where at least 30 percent of annual precipitation was received as snow, and streamflow data were restricted to regionally based winter-spring periods to focus the analyses on snowmelt-related streamflow. Trends over time in WSCVD at gages in the eastern U.S. were relatively homogenous in magnitude and direction and statistically significant; median WSCVD was earlier by 8.2 days (1.1 days/decade) and 8.6 days (1.6 days/decade) for 1940–2014 and 1960–2014 periods respectively. Fewer trends in the West were significant though most trends indicated earlier WSCVD over time. Trends at low-to-mid elevation (<1600 m) basins in the West, predominantly located in the Northwest, had median earlier WSCVD by 6.8 days (1940–2014, 0.9 days/decade) and 3.4 days (1960–2014, 0.6 days/decade). Streamflow timing at high-elevation (⩾1600 m) basins in the West had median earlier WSCVD by 4.0 days (1940–2014, 0.5 days/decade) and 5.2 days (1960–2014, 0.9 days/decade). Trends toward earlier WSCVD in the Northwest were not statistically significant, differing from previous studies that observed many large and (or) significant trends in this region. Much of this difference is likely due to the sensitivity of trend tests to the time period being tested, as well as differences in the streamflow timing metrics used among the studies. Mean February–May air temperature was significantly correlated with WSCVD at 100 percent of the study gages (field significant, p < 0.0001), demonstrating the sensitivity of WSCVD to air temperature across snowmelt dominated basins in the U.S. WSCVD in high elevation basins in the West, however, was related to both air temperature and precipitation yielding earlier snowmelt-related streamflow timing under warmer and drier conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.051","usgsCitation":"Dudley, R.W., Hodgkins, G.A., McHale, M., Kolian, M., and Renard, B., 2017, Trends in snowmelt-related streamflow timing in the conterminous United States: Journal of Hydrology, v. 547, p. 208-221, https://doi.org/10.1016/j.jhydrol.2017.01.051.","productDescription":"14 p.","startPage":"208","endPage":"221","ipdsId":"IP-076605","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":469955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.01.051","text":"Publisher Index Page"},{"id":356297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"547","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc6f5e4b0f5d57878ebad","contributors":{"authors":[{"text":"Dudley, Robert W. 0000-0002-0934-0568 rwdudley@usgs.gov","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":2223,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert","email":"rwdudley@usgs.gov","middleInitial":"W.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":177292,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolian, Michael J.","contributorId":177290,"corporation":false,"usgs":false,"family":"Kolian","given":"Michael J.","affiliations":[],"preferred":false,"id":669223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":669224,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261469,"text":"70261469 - 2017 - Global nonfuel mineral exploration trends 2001-2015","interactions":[],"lastModifiedDate":"2024-12-11T17:05:53.664076","indexId":"70261469","displayToPublicDate":"2017-04-01T11:03:06","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2755,"text":"Mining Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Global nonfuel mineral exploration trends 2001-2015","docAbstract":"This review summarizes significant exploration trends related to active sites and budgets, mineral commodities and regional factors for the years 2001-2015. Data were compiled by specialists in the USGS-NMIC, and reported annually in the USGS-NMIC Minerals Yearbook series and in the May issue of Mining Engineering magazine. External data for these analyses were derived from industry sources, published literature, and SNL Metals & Mining, an offering of S&P Global Market Intelligence (New York, NY).","language":"English","publisher":"Society for Mining, Metallurgy, & Exploration","usgsCitation":"Karl, N.A., and Wilburn, D.R., 2017, Global nonfuel mineral exploration trends 2001-2015: Mining Engineering, v. 69, no. 4, p. 30-37.","productDescription":"8 p.","startPage":"30","endPage":"37","ipdsId":"IP-081372","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":464991,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://me.smenet.org/abstract.cfm?preview=1&articleID=7496&page=30"},{"id":465022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karl, Nick A 0000-0003-2858-2498","orcid":"https://orcid.org/0000-0003-2858-2498","contributorId":246006,"corporation":false,"usgs":true,"family":"Karl","given":"Nick","email":"","middleInitial":"A","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":920665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilburn, David R. 0000-0002-5371-7617 wilburn@usgs.gov","orcid":"https://orcid.org/0000-0002-5371-7617","contributorId":1755,"corporation":false,"usgs":true,"family":"Wilburn","given":"David","email":"wilburn@usgs.gov","middleInitial":"R.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":920666,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192945,"text":"70192945 - 2017 - Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration","interactions":[],"lastModifiedDate":"2025-12-23T14:37:28.701523","indexId":"70192945","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration","docAbstract":"Regional classification of streams is an early step in the Ecological Limits of Hydrologic Alteration framework. Many stream classifications are based on an inductive approach using hydrologic data from minimally disturbed basins, but this approach may underrepresent streams from heavily disturbed basins or sparsely gaged arid regions. An alternative is a deductive approach, using watershed climate, land use, and geomorphology to classify streams, but this approach may miss important hydrological characteristics of streams. We classified all stream reaches in California using both approaches. First, we used Bayesian and hierarchical clustering to classify reaches according to watershed characteristics. Streams were clustered into seven classes according to elevation, sedimentary rock, and winter precipitation. Permutation-based analysis of variance and random forest analyses were used to determine which hydrologic variables best separate streams into their respective classes. Stream typology (i.e., the class that a stream reach is assigned to) is shaped mainly by patterns of high and mean flow behavior within the stream's landscape context. Additionally, random forest was used to determine which hydrologic variables best separate minimally disturbed reference streams from non-reference streams in each of the seven classes. In contrast to stream typology, deviation from reference conditions is more difficult to detect and is largely defined by changes in low-flow variables, average daily flow, and duration of flow. Our combined deductive/inductive approach allows us to estimate flow under minimally disturbed conditions based on the deductive analysis and compare to measured flow based on the inductive analysis in order to estimate hydrologic change.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1802","usgsCitation":"Pyne, M.I., Carlisle, D.M., Konrad, C.P., and Stein, E.D., 2017, Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration: Ecohydrology, v. 10, no. 3, e1802, 14 p; Data Release, https://doi.org/10.1002/eco.1802.","productDescription":"e1802, 14 p; Data Release","ipdsId":"IP-073147","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":348846,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Select watershed attributes for California stream segments (NHDPlus V.1)"},{"id":348662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"10","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-02","publicationStatus":"PW","scienceBaseUri":"5a60fbeee4b06e28e9c237a8","contributors":{"authors":[{"text":"Pyne, Matthew I.","contributorId":198847,"corporation":false,"usgs":false,"family":"Pyne","given":"Matthew","email":"","middleInitial":"I.","affiliations":[{"id":24666,"text":"Lamar University","active":true,"usgs":false}],"preferred":false,"id":717396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":717395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stein, Eric D.","contributorId":198848,"corporation":false,"usgs":false,"family":"Stein","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":12704,"text":"Southern California Coastal Water Research Project","active":true,"usgs":false}],"preferred":false,"id":717398,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192500,"text":"70192500 - 2017 - Seasonal survival of adult female mottled ducks","interactions":[],"lastModifiedDate":"2017-10-26T14:34:00","indexId":"70192500","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal survival of adult female mottled ducks","docAbstract":"The mottled duck (Anas fulgivula) is a non-migratory duck dependent on coastal habitats to meet all of its life cycle requirements in the Western Gulf Coast (WGC) of Texas and Louisiana, USA. This population of mottled ducks has experienced a moderate decline during the past 2 decades. Adult survival has been identified as an important factor influencing population demography. Previous work based on band-recovery data has provided only annual estimates of survival. We assessed seasonal patterns of female mottled duck survival from 2009 to 2012 using individuals marked with satellite platform transmitter terminals (PTTs). We used temperature and movement sensors within each PTT to indicate potential mortality events. We estimated cumulative weekly survival and ranked factors influential in patterns of mortality using known-fate modeling in Program MARK. Models included 4 predictors: week; hunting and non-hunting periods; biological periods defined as breeding, brooding, molt, and pairing; and mass at time of capture. Models containing hunt periods, during and outside the mottled duck season, comprised essentially 100% of model weights where both legal and illegal harvest had a negative influence on mottled duck survival. Survival rates were low during 2009–2011 (12–38% annual rate of survival), when compared with the long-term banding average of 53% annual survival. During 2011, survival of female mottled ducks was the lowest annual rate (12%) ever documented and coincided with extreme drought. Management actions maximizing the availability of wetlands and associated upland habitats during hunting seasons and drought conditions may increase adult female mottled duck survival.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21221","usgsCitation":"Moon, J.A., Haukos, D.A., and Conway, W.C., 2017, Seasonal survival of adult female mottled ducks: Journal of Wildlife Management, v. 81, no. 3, p. 461-469, https://doi.org/10.1002/jwmg.21221.","productDescription":"9 p.","startPage":"461","endPage":"469","ipdsId":"IP-064529","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":461669,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21221","text":"Publisher Index Page"},{"id":347493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Chenier Plain Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.086669921875,\n 29.480252193344267\n ],\n [\n -93.74359130859375,\n 29.480252193344267\n ],\n [\n -93.74359130859375,\n 30.375244781665323\n ],\n [\n -95.086669921875,\n 30.375244781665323\n ],\n [\n -95.086669921875,\n 29.480252193344267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-09","publicationStatus":"PW","scienceBaseUri":"5a07e910e4b09af898c8cbf1","contributors":{"authors":[{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":716433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":716434,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193709,"text":"70193709 - 2017 - Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation","interactions":[],"lastModifiedDate":"2017-11-29T16:10:58","indexId":"70193709","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation","docAbstract":"Environmental DNA (eDNA) can be used as an assessment tool to detect populations of threatened species and provide fine-scale data required to make management decisions. The objectives of this project were to use quantitative PCR (qPCR) to: (i) detect spiked salamander DNA in soil, (ii) quantify eDNA degradation over time, (iii) determine detectability of salamander eDNA in a terrestrial environment using soil, faeces, and skin swabs, (iv) detect salamander eDNA in a mesocosm experiment. Salamander eDNA was positively detected in 100% of skin swabs and 66% of faecal samples and concentrations did not differ between the two sources. However, eDNA was not detected in soil samples collected from directly underneath wild-caught living salamanders. Salamander genomic DNA (gDNA) was detected in all qPCR reactions when spiked into soil at 10.0, 5.0, and 1.0 ng/g soil and spike concentration had a significant effect on detected concentrations. Only 33% of samples showed recoverable eDNA when spiked with 0.25 ng/g soil, which was the low end of eDNA detection. To determine the rate of eDNA degradation, gDNA (1 ng/g soil) was spiked into soil and quantified over seven days. Salamander eDNA concentrations decreased across days, but eDNA was still amplifiable at day 7. Salamander eDNA was detected in two of 182 mesocosm soil samples over 12 weeks (n = 52 control samples; n = 65 presence samples; n = 65 eviction samples). The discrepancy in detection success between experiments indicates the potential challenges for this method to be used as a monitoring technique for small-bodied wild terrestrial salamander populations.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.12667","usgsCitation":"Walker, D.M., Leys, J.E., Dunham, K.E., Oliver, J.C., Schiller, E.E., Stephenson, K.S., Kimrey, J.T., Wooten, J., and Rogers, M.W., 2017, Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation: Molecular Ecology Resources, v. 17, no. 6, p. 1223-1230, https://doi.org/10.1111/1755-0998.12667.","productDescription":"8 p.","startPage":"1223","endPage":"1230","ipdsId":"IP-080810","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-11","publicationStatus":"PW","scienceBaseUri":"5a003150e4b0531197b5a748","contributors":{"authors":[{"text":"Walker, Donald M.","contributorId":39132,"corporation":false,"usgs":false,"family":"Walker","given":"Donald","email":"","middleInitial":"M.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leys, Jacob E.","contributorId":199800,"corporation":false,"usgs":false,"family":"Leys","given":"Jacob","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunham, Kelly E.","contributorId":169093,"corporation":false,"usgs":false,"family":"Dunham","given":"Kelly","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Joshua C.","contributorId":199613,"corporation":false,"usgs":false,"family":"Oliver","given":"Joshua","email":"","middleInitial":"C.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schiller, Emily E.","contributorId":145533,"corporation":false,"usgs":false,"family":"Schiller","given":"Emily","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, Kelsey S.","contributorId":100992,"corporation":false,"usgs":false,"family":"Stephenson","given":"Kelsey","email":"","middleInitial":"S.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kimrey, John T.","contributorId":199571,"corporation":false,"usgs":false,"family":"Kimrey","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720395,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wooten, Jessica","contributorId":190940,"corporation":false,"usgs":false,"family":"Wooten","given":"Jessica","email":"","affiliations":[{"id":35654,"text":"Centre College, Danville, KY, USA","active":true,"usgs":false}],"preferred":false,"id":720396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720397,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70192160,"text":"70192160 - 2017 - Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","interactions":[],"lastModifiedDate":"2017-10-23T13:54:01","indexId":"70192160","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","docAbstract":"The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2017.01.019","usgsCitation":"Xiong, J., Thenkabail, P.S., Gumma, M., Teluguntla, P.G., Poehnelt, J., Congalton, R.G., Yadav, K., and Thau, D., 2017, Automated cropland mapping of continental Africa using Google Earth Engine cloud computing: ISPRS Journal of Photogrammetry and Remote Sensing, v. 126, p. 225-244, https://doi.org/10.1016/j.isprsjprs.2017.01.019.","productDescription":"20 p.","startPage":"225","endPage":"244","ipdsId":"IP-081308","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469973,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2017.01.019","text":"Publisher Index Page"},{"id":347130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -18.80859375,\n -36.03133177633187\n ],\n [\n 52.03125,\n -36.03133177633187\n ],\n [\n 52.03125,\n 37.579412513438385\n ],\n [\n -18.80859375,\n 37.579412513438385\n ],\n [\n -18.80859375,\n -36.03133177633187\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59eeffa9e4b0220bbd988fa9","contributors":{"authors":[{"text":"Xiong, Jun 0000-0002-2320-0780 jxiong@usgs.gov","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":5276,"corporation":false,"usgs":true,"family":"Xiong","given":"Jun","email":"jxiong@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gumma, Murali Krishna","contributorId":50426,"corporation":false,"usgs":true,"family":"Gumma","given":"Murali Krishna","affiliations":[],"preferred":false,"id":714481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi G. 0000-0001-8060-9841 pteluguntla@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":5275,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","email":"pteluguntla@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poehnelt, Justin 0000-0001-5914-4269","orcid":"https://orcid.org/0000-0001-5914-4269","contributorId":192328,"corporation":false,"usgs":false,"family":"Poehnelt","given":"Justin","email":"","affiliations":[],"preferred":false,"id":714483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Congalton, Russell G.","contributorId":138718,"corporation":false,"usgs":false,"family":"Congalton","given":"Russell","email":"","middleInitial":"G.","affiliations":[{"id":12507,"text":"Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA","active":true,"usgs":false}],"preferred":false,"id":714484,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yadav, Kamini","contributorId":138720,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","affiliations":[{"id":12507,"text":"Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA","active":true,"usgs":false}],"preferred":false,"id":714485,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thau, David","contributorId":103581,"corporation":false,"usgs":true,"family":"Thau","given":"David","email":"","affiliations":[],"preferred":false,"id":714878,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193667,"text":"70193667 - 2017 - Estimating occupancy probability of moose using hunter survey data","interactions":[],"lastModifiedDate":"2017-11-06T11:06:34","indexId":"70193667","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occupancy probability of moose using hunter survey data","docAbstract":"Monitoring rare species can be difficult, especially across large spatial extents, making conventional methods of population monitoring costly and logistically challenging. Citizen science has the potential to produce observational data across large areas that can be used to monitor wildlife distributions using occupancy models. We used citizen science (i.e., hunter surveys) to facilitate monitoring of moose (Alces alces) populations, an especially important endeavor because of their recent apparent declines in the northeastern and upper midwestern regions of the United States. To better understand patterns of occurrence of moose in New York, we used data collected through an annual survey of approximately 11,000 hunters between 2012 and 2014 that recorded detection–non-detection data of moose and other species. We estimated patterns of occurrence of moose in relation to land cover characteristics, climate effects, and interspecific interactions using occupancy models to analyze spatially referenced moose observations. Coniferous and deciduous forest with low prevalence of white-tailed deer (Odocoileus virginianus) had the highest probability of moose occurrence. This study highlights the potential of data collected using citizen science for understanding the spatial distribution of low-density species across large spatial extents and providing key information regarding where and when future research and management activities should be focused.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21207","usgsCitation":"Crum, N.J., Fuller, A.K., Sutherland, C.S., Cooch, E.G., and Hurst, J.E., 2017, Estimating occupancy probability of moose using hunter survey data: Journal of Wildlife Management, v. 81, no. 3, p. 521-534, https://doi.org/10.1002/jwmg.21207.","productDescription":"14 p.","startPage":"521","endPage":"534","ipdsId":"IP-074160","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":461649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21207","text":"Publisher Index Page"},{"id":348253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.365966796875,\n 42.0125705565935\n ],\n [\n -73.267822265625,\n 42.0125705565935\n ],\n [\n -73.267822265625,\n 45.00753503123719\n ],\n [\n -76.365966796875,\n 45.00753503123719\n ],\n [\n -76.365966796875,\n 42.0125705565935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-28","publicationStatus":"PW","scienceBaseUri":"5a07e90fe4b09af898c8cbe9","contributors":{"authors":[{"text":"Crum, Nathan J.","contributorId":200016,"corporation":false,"usgs":false,"family":"Crum","given":"Nathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutherland, Christopher S.","contributorId":139375,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":720655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cooch, Evan G.","contributorId":100673,"corporation":false,"usgs":true,"family":"Cooch","given":"Evan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":720656,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hurst, Jeremy E.","contributorId":177504,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy","email":"","middleInitial":"E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":720657,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188346,"text":"70188346 - 2017 - Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures","interactions":[],"lastModifiedDate":"2017-06-06T16:08:28","indexId":"70188346","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures","docAbstract":"Stable carbon and hydrogen isotope signatures of methane, water, and inorganic carbon are widely utilized in natural gas systems for distinguishing microbial and thermogenic methane and for delineating methanogenic pathways (acetoclastic, hydrogenotrophic, and/or methylotrophic methanogenesis). Recent studies of coal and shale gas systems have characterized in situ microbial communities and provided stable isotope data (δD-CH4, δD-H2O, δ13C-CH4, and δ13C-CO2) from a wider range of environments than available previously. Here we review the principal biogenic methane-yielding pathways in coal beds and shales and the isotope effects imparted on methane, document the uncertainties and inconsistencies in established isotopic fingerprinting techniques, and identify the knowledge gaps in understanding the subsurface processes that govern H and C isotope signatures of biogenic methane. We also compare established isotopic interpretations with recent microbial community characterization techniques, which reveal additional inconsistencies in the interpretation of microbial metabolic pathways in coal beds and shales. Collectively, the re-assessed data show that widely-utilized isotopic fingerprinting techniques neglect important complications in coal beds and shales.
Isotopic fingerprinting techniques that combine δ13C-CH4 with δD-CH4 and/or δ13C-CO2have significant limitations: (1) The consistent ~ 160‰ offset between δD-H2O and δD-CH4 could imply that hydrogenotrophic methanogenesis is the dominant metabolic pathway in microbial gas systems. However, hydrogen isotopes can equilibrate between methane precursors and coexisting water, yielding a similar apparent H isotope signal as hydrogenotrophic methanogenesis, regardless of the actual methane formation pathway. (2) Non-methanogenic processes such as sulfate reduction, Fe oxide reduction, inputs of thermogenic methane, anaerobic methane oxidation, and/or formation water interaction can cause the apparent carbon isotope fractionation between δ13C-CH4 and δ13C-CO2(α13CCO2-CH4) to differ from the true methanogenic fractionation, complicating interpretation of methanogenic pathways. (3) Where little-fractionating non-methanogenic bacterial processes compete with highly-fractionating methanogenesis, the mass balance between CH4 and CO2 is affected. This has implications for δ13C values and provides an alternative interpretation for net C isotope signatures than solely the pathways used by active methanogens. (4) While most of the reviewed values of δD-H2O - δD-CH4 and α13CCO2-CH4 are apparently consistent with hydrogenotrophic methanogenesis as the dominant pathway in coal beds and shales, recent microbial community characterization techniques suggest a possible role for acetoclastic or methylotrophic methanogenesis in some basins.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2017.01.027","usgsCitation":"Vinson, D.S., Blair, N.E., Martini, A.M., Larter, S., Orem, W.H., and McIntosh, J.C., 2017, Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures: Chemical Geology, v. 453, p. 128-145, https://doi.org/10.1016/j.chemgeo.2017.01.027.","productDescription":"18 p.","startPage":"128","endPage":"145","ipdsId":"IP-073590","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469977,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2017.01.027","text":"Publisher Index Page"},{"id":342186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"453","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2de4b0f6c2d0d9c756","contributors":{"authors":[{"text":"Vinson, David S.","contributorId":172390,"corporation":false,"usgs":false,"family":"Vinson","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":25392,"text":"Department of Geography and Earth Science, University of North Carolina at Charlotte, North Carolina, USA","active":true,"usgs":false}],"preferred":false,"id":697341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blair, Neal E.","contributorId":192674,"corporation":false,"usgs":false,"family":"Blair","given":"Neal","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":697342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martini, Anna M.","contributorId":192675,"corporation":false,"usgs":false,"family":"Martini","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":35249,"text":"Department of Geology, Amherst College","active":true,"usgs":false}],"preferred":false,"id":697343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larter, Steve","contributorId":192676,"corporation":false,"usgs":false,"family":"Larter","given":"Steve","email":"","affiliations":[],"preferred":false,"id":697344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":697340,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McIntosh, Jennifer C.","contributorId":139870,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":13301,"text":"Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona","active":true,"usgs":false}],"preferred":false,"id":697345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191872,"text":"70191872 - 2017 - Urbanization may limit impacts of an invasive predator on native mammal diversity","interactions":[],"lastModifiedDate":"2017-10-18T14:45:36","indexId":"70191872","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization may limit impacts of an invasive predator on native mammal diversity","docAbstract":"Aim
Our understanding of the effects of invasive species on faunal diversity is limited in part because invasions often occur in modified landscapes where other drivers of community diversity can exacerbate or reduce the net impacts of an invader. Furthermore, rigorous assessments of the effects of invasive species on native communities that account for variation in sampling, species-specific detection and occurrence of rare species are lacking. Invasive Burmese pythons (Python molurus bivittatus) may be causing declines in medium- to large-sized mammals throughout the Greater Everglades Ecosystem (GEE); however, other factors such as urbanization, habitat changes and drastic alteration in water flow may also be influential in structuring mammal communities. The aim of this study was to gain an understanding of how mammal communities simultaneously facing invasive predators and intensively human-altered landscapes are influenced by these drivers and their interactions.
Location
Florida, USA.
Methods
We used data from trail cameras and scat searches with a hierarchical community model that accounts for undetected species to determine the relative influence of introduced Burmese pythons, urbanization, local hydrology, habitat types and interactive effects between pythons and urbanization on mammal species occurrence, site-level species richness, and turnover.
Results
Python density had significant negative effects on all species except coyotes. Despite these negative effects, occurrence of some generalist species increased significantly near urban areas. At the community level, pythons had the greatest impact on species richness, while turnover was greatest along the urbanization gradient where communities were increasingly similar as distance to urbanization decreased.
Main conclusions
We found evidence for an antagonistic interaction between pythons and urbanization where the impacts of pythons were reduced near urban development. Python-induced changes to mammal communities may be mediated near urban development, but elsewhere in the GEE, pythons are likely causing a fundamental restructuring of the food web, declines in ecosystem function, and creating complex and unpredictable cascading effects.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.12531","usgsCitation":"Reichert, B., Sovie, A.R., Udell, B.J., Hart, K.M., Borkhataria, R.R., Bonneau, M., Reed, R., and McCleery, R.A., 2017, Urbanization may limit impacts of an invasive predator on native mammal diversity: Diversity and Distributions, v. 23, no. 4, p. 355-367, https://doi.org/10.1111/ddi.12531.","productDescription":"13 p.","startPage":"355","endPage":"367","ipdsId":"IP-077761","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.12531","text":"Publisher Index Page"},{"id":346891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.529296875,\n 25.085598897064752\n ],\n [\n -80.0189208984375,\n 25.085598897064752\n ],\n [\n -80.0189208984375,\n 27.235094607795503\n ],\n [\n -82.529296875,\n 27.235094607795503\n ],\n [\n -82.529296875,\n 25.085598897064752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-26","publicationStatus":"PW","scienceBaseUri":"59e86836e4b05fe04cd4d1ff","contributors":{"authors":[{"text":"Reichert, Brian E.","contributorId":197423,"corporation":false,"usgs":false,"family":"Reichert","given":"Brian E.","affiliations":[],"preferred":false,"id":713475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sovie, Adia R.","contributorId":197424,"corporation":false,"usgs":false,"family":"Sovie","given":"Adia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":713477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Udell, Brad J.","contributorId":197490,"corporation":false,"usgs":false,"family":"Udell","given":"Brad","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":713606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":713478,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borkhataria, Rena R.","contributorId":197425,"corporation":false,"usgs":false,"family":"Borkhataria","given":"Rena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":713479,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonneau, Mathieu","contributorId":150041,"corporation":false,"usgs":false,"family":"Bonneau","given":"Mathieu","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":713480,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713474,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCleery, Robert A.","contributorId":139849,"corporation":false,"usgs":false,"family":"McCleery","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":713476,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187329,"text":"70187329 - 2017 - A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations","interactions":[],"lastModifiedDate":"2017-04-28T15:43:43","indexId":"70187329","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations","docAbstract":"Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/690444","usgsCitation":"Ward, A.S., Kelleher, C.A., Mason, S.J., Wagener, T., McIntyre, N., McGlynn, B.L., Runkel, R.L., and Payn, R.A., 2017, A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations: Freshwater Science, v. 36, no. 1, p. 195-217, https://doi.org/10.1086/690444.","productDescription":"23 p.","startPage":"195","endPage":"217","ipdsId":"IP-074821","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":461661,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/2ec1a71e-046a-4faa-ad85-2f323af51119","text":"External Repository"},{"id":340632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590454a1e4b022cee40dc222","contributors":{"authors":[{"text":"Ward, Adam S.","contributorId":11508,"corporation":false,"usgs":true,"family":"Ward","given":"Adam","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelleher, Christa A.","contributorId":46417,"corporation":false,"usgs":true,"family":"Kelleher","given":"Christa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":693394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mason, Seth J. K.","contributorId":191535,"corporation":false,"usgs":false,"family":"Mason","given":"Seth","email":"","middleInitial":"J. K.","affiliations":[],"preferred":false,"id":693395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagener, Thorsten","contributorId":176323,"corporation":false,"usgs":false,"family":"Wagener","given":"Thorsten","email":"","affiliations":[],"preferred":false,"id":693396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntyre, Neil","contributorId":191602,"corporation":false,"usgs":false,"family":"McIntyre","given":"Neil","email":"","affiliations":[],"preferred":false,"id":693397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGlynn, Brian L.","contributorId":83012,"corporation":false,"usgs":true,"family":"McGlynn","given":"Brian","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693392,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Payn, Robert A.","contributorId":36461,"corporation":false,"usgs":true,"family":"Payn","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":693399,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187205,"text":"70187205 - 2017 - Scale-specific habitat relationships influence patch occupancy: defining neighborhoods to optimize the effectiveness of landscape-scale grassland bird conservation","interactions":[],"lastModifiedDate":"2017-04-26T12:45:06","indexId":"70187205","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Scale-specific habitat relationships influence patch occupancy: defining neighborhoods to optimize the effectiveness of landscape-scale grassland bird conservation","docAbstract":"Context
Beyond the recognized importance of protecting large areas of contiguous habitat, conservation efforts for many species are complicated by the fact that patch suitability may also be affected by characteristics of the landscape within which the patch is located. Currently, little is known about the spatial scales at which species respond to different aspects of the landscape surrounding an occupied patch.
Objectives
Using grassland bird point count data, we describe an approach to evaluating scale-specific effects of landscape composition on patch occupancy.
Methods
We used data from 793 point count surveys conducted in idle and grazed grasslands across Wisconsin, USA from 2012 to 2014 to evaluate scale-dependencies in the response of grassland birds to landscape composition. Patch occupancy models were used to evaluate the relationship between occupancy and landscape composition at scales from 100 to 3000 m.
Results
Bobolink (Dolichonyx oryzivorus) exhibited a pattern indicating selection for grassland habitats in the surrounding landscape at all spatial scales while selecting against other habitats. Eastern Meadowlark (Sturnella magna) displayed evidence of scale sensitivity for all habitat types. Grasshopper Sparrow (Ammodramus savannarum) showed a strong positive response to pasture and idle grass at all scales and negatively to cropland at large scales. Unlike other species, patch occupancy by Henslow’s Sparrow (A. henslowii) was primarily influenced by patch area.
Conclusions
Our results suggest that both working grasslands (pasture) and idle conservation grasslands can play an important role in grassland bird conservation but also highlight the importance of considering species-specific patch and landscape characteristics for effective conservation.
Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-16-0012.1","usgsCitation":"Liu, S., Bond-Lamberty, B., Boysen, L.R., Ford, J.D., Fox, A., Gallo, K., Hatfield, J.L., Henebry, G.M., Huntington, T.G., Liu, Z., Loveland, T.R., Norby, R.J., Sohl, T.L., Steiner, A.L., Yuan, W., Zhang, Z., and Zhao, S., 2017, Grand challenges in understanding the interplay of climate and land changes: Earth Interactions, v. 21, p. 1-43, https://doi.org/10.1175/EI-D-16-0012.1.","productDescription":"Paper No. 2; 43 p.","startPage":"1","endPage":"43","ipdsId":"IP-073337","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469960,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/11858/00-001M-0000-002D-26BD-F","text":"External 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MD","active":true,"usgs":false},{"id":13566,"text":"Joint Global Change Research Institute, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":723948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boysen, Lena R.","contributorId":200963,"corporation":false,"usgs":false,"family":"Boysen","given":"Lena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":723949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, James D.","contributorId":200964,"corporation":false,"usgs":false,"family":"Ford","given":"James","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":723950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Andrew","contributorId":190103,"corporation":false,"usgs":false,"family":"Fox","given":"Andrew","affiliations":[],"preferred":false,"id":723951,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gallo, Kevin 0000-0001-9162-5011 kgallo@usgs.gov","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":192334,"corporation":false,"usgs":true,"family":"Gallo","given":"Kevin","email":"kgallo@usgs.gov","affiliations":[],"preferred":true,"id":723952,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hatfield, Jerry L.","contributorId":71082,"corporation":false,"usgs":true,"family":"Hatfield","given":"Jerry","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723953,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henebry, Geoffrey M.","contributorId":124528,"corporation":false,"usgs":false,"family":"Henebry","given":"Geoffrey","email":"","middleInitial":"M.","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":723954,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723944,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Liu, Zhihua","contributorId":105228,"corporation":false,"usgs":true,"family":"Liu","given":"Zhihua","email":"","affiliations":[],"preferred":false,"id":723955,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":723956,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Norby, Richard J. 0000-0002-0238-9828","orcid":"https://orcid.org/0000-0002-0238-9828","contributorId":167836,"corporation":false,"usgs":false,"family":"Norby","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":723957,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":723958,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Steiner, Allison L.","contributorId":49261,"corporation":false,"usgs":true,"family":"Steiner","given":"Allison","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723959,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Yuan, Wenping","contributorId":83435,"corporation":false,"usgs":true,"family":"Yuan","given":"Wenping","email":"","affiliations":[],"preferred":false,"id":723960,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zhang, Zhao","contributorId":200965,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhao","email":"","affiliations":[],"preferred":false,"id":723961,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zhao, Shuqing","contributorId":9152,"corporation":false,"usgs":true,"family":"Zhao","given":"Shuqing","email":"","affiliations":[],"preferred":false,"id":723962,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70189479,"text":"70189479 - 2017 - Community stability within the St. Marys River fish community: Evidence from trawl surveys","interactions":[],"lastModifiedDate":"2018-03-28T11:22:52","indexId":"70189479","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Community stability within the St. Marys River fish community: Evidence from trawl surveys","docAbstract":"A trawl survey was conducted in the Saint Marys River during 2010–2011 and we compared our results to a prior trawl survey conducted during 1979–1983 to look for long-term changes in the fish community, especially in terms of changes induced by invasive species. We found no substantive temporal differences in fish density, fish biomass, or fish diversity; lower trawl biomass during 2010–2011 was likely a result of day versus night trawling. The Saint Marys River remains a center of high fish diversity, invasive species remain rare, and the system continues to exhibit overall long-term stability. Trawling captured a wide range of fish species, but was likely not an effective stock assessment tool for managed game fish because catch rates were low or variable for all game species except yellow perch. Trawling appeared to be an effective tool for sampling connecting channel diversity, especially when large numbers of individuals are needed for directed studies, but annual sampling would be needed to use data to assess recruitment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.10.014","usgsCitation":"Schaeffer, J.S., Bowen, A.K., and Fielder, D.G., 2017, Community stability within the St. Marys River fish community: Evidence from trawl surveys: Journal of Great Lakes Research, v. 43, no. 2, p. 399-404, https://doi.org/10.1016/j.jglr.2016.10.014.","productDescription":"6 p.","startPage":"399","endPage":"404","ipdsId":"IP-074635","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2016.10.014","text":"Publisher Index Page"},{"id":343814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"St. Marys River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.6441650390625,\n 45.920587344733654\n ],\n [\n -83.529052734375,\n 45.920587344733654\n ],\n [\n -83.529052734375,\n 46.558860303117164\n ],\n [\n -84.6441650390625,\n 46.558860303117164\n ],\n [\n -84.6441650390625,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5968869ee4b0d1f9f05f597a","contributors":{"authors":[{"text":"Schaeffer, Jeffrey S.","contributorId":89083,"corporation":false,"usgs":true,"family":"Schaeffer","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":704875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowen, Anjanette K.","contributorId":27398,"corporation":false,"usgs":true,"family":"Bowen","given":"Anjanette","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":704876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fielder, David G.","contributorId":127535,"corporation":false,"usgs":false,"family":"Fielder","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":7024,"text":"Michigan Department of Natural Resources, Fisheries Research Station","active":true,"usgs":false}],"preferred":false,"id":704877,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192050,"text":"70192050 - 2017 - Standard methods for sampling freshwater fishes: Opportunities for international collaboration","interactions":[],"lastModifiedDate":"2018-02-28T14:31:23","indexId":"70192050","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Standard methods for sampling freshwater fishes: Opportunities for international collaboration","docAbstract":"With publication of Standard Methods for Sampling North American Freshwater Fishes in 2009, the American Fisheries Society (AFS) recommended standard procedures for North America. To explore interest in standardizing at intercontinental scales, a symposium attended by international specialists in freshwater fish sampling was convened at the 145th Annual AFS Meeting in Portland, Oregon, in August 2015. Participants represented all continents except Australia and Antarctica and were employed by state and federal agencies, universities, nongovernmental organizations, and consulting businesses. Currently, standardization is practiced mostly in North America and Europe. Participants described how standardization has been important for management of long-term data sets, promoting fundamental scientific understanding, and assessing efficacy of large spatial scale management strategies. Academics indicated that standardization has been useful in fisheries education because time previously used to teach how sampling methods are developed is now more devoted to diagnosis and treatment of problem fish communities. Researchers reported that standardization allowed increased sample size for method validation and calibration. Group consensus was to retain continental standards where they currently exist but to further explore international and intercontinental standardization, specifically identifying where synergies and bridges exist, and identify means to collaborate with scientists where standardization is limited but interest and need occur.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/03632415.2017.1276352","usgsCitation":"Bonar, S.A., Mercado-Silva, N., Hubert, W.A., Beard, Dave, G., Kubecka, J., Graeb, B.D., Lester, N.P., Porath, M.T., and Winfield, I.J., 2017, Standard methods for sampling freshwater fishes: Opportunities for international collaboration: Fisheries, v. 42, no. 3, p. 150-156, https://doi.org/10.1080/03632415.2017.1276352.","productDescription":"7 p.","startPage":"150","endPage":"156","ipdsId":"IP-077095","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469967,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1080/03632415.2017.1276352","text":"External Repository"},{"id":346973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"59e9b995e4b05fe04cd65c9c","contributors":{"authors":[{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mercado-Silva, Norman","contributorId":18219,"corporation":false,"usgs":true,"family":"Mercado-Silva","given":"Norman","email":"","affiliations":[],"preferred":false,"id":714013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hubert, Wayne A.","contributorId":9325,"corporation":false,"usgs":true,"family":"Hubert","given":"Wayne","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":714010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dave, Goran","contributorId":197665,"corporation":false,"usgs":false,"family":"Dave","given":"Goran","email":"","affiliations":[],"preferred":false,"id":714015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kubecka, Jan","contributorId":197666,"corporation":false,"usgs":false,"family":"Kubecka","given":"Jan","email":"","affiliations":[],"preferred":false,"id":714016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Graeb, Brian D. S.","contributorId":171851,"corporation":false,"usgs":false,"family":"Graeb","given":"Brian","email":"","middleInitial":"D. S.","affiliations":[{"id":26956,"text":"Departement of Natural Resource Management, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":714017,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lester, Nigel P.","contributorId":101544,"corporation":false,"usgs":true,"family":"Lester","given":"Nigel","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":714018,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Porath, Mark T.","contributorId":28846,"corporation":false,"usgs":true,"family":"Porath","given":"Mark","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":714019,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Winfield, Ian J.","contributorId":197667,"corporation":false,"usgs":false,"family":"Winfield","given":"Ian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":714020,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70192128,"text":"70192128 - 2017 - From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments","interactions":[],"lastModifiedDate":"2017-10-23T14:56:58","indexId":"70192128","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments","docAbstract":"Our different kinds of minds and types of thinking affect the ways we decide, take action, and cooperate (or not). Derived from these types of minds, innate biases, beliefs, heuristics, and values (BBHV) influence behaviors, often beneficially, when individuals or small groups face immediate, local, acute situations that they and their ancestors faced repeatedly in the past. BBHV, though, need to be recognized and possibly countered or used when facing new, complex issues or situations especially if they need to be managed for the benefit of a wider community, for the longer-term and the larger-scale. Taking BBHV into account, we explain and provide a cyclic science-infused adaptive framework for (1) gaining knowledge of complex systems and (2) improving their management. We explore how this process and framework could improve the governance of science and policy for different types of systems and issues, providing examples in the area of natural resources, hazards, and the environment. Lastly, we suggest that an “Open Traceable Accountable Policy” initiative that followed our suggested adaptive framework could beneficially complement recent Open Data/Model science initiatives.
","language":"English","publisher":"AGU","doi":"10.1002/2016EF000487","usgsCitation":"Glynn, P.D., Voinov, A.A., Shapiro, C.D., and White, P.A., 2017, From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments: Earth's Future, v. 5, no. 4, p. 356-378, https://doi.org/10.1002/2016EF000487.","productDescription":"33 p.","startPage":"356","endPage":"378","ipdsId":"IP-083142","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469968,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016ef000487","text":"Publisher Index Page"},{"id":347146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-24","publicationStatus":"PW","scienceBaseUri":"59eeffa9e4b0220bbd988fac","contributors":{"authors":[{"text":"Glynn, Pierre D. 0000-0001-8804-7003 pglynn@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":2141,"corporation":false,"usgs":true,"family":"Glynn","given":"Pierre","email":"pglynn@usgs.gov","middleInitial":"D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":714336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voinov, Alexey A.","contributorId":197796,"corporation":false,"usgs":false,"family":"Voinov","given":"Alexey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shapiro, Carl D. 0000-0002-1598-6808 cshapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-1598-6808","contributorId":3048,"corporation":false,"usgs":true,"family":"Shapiro","given":"Carl","email":"cshapiro@usgs.gov","middleInitial":"D.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":714338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Paul A.","contributorId":197797,"corporation":false,"usgs":false,"family":"White","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714339,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192086,"text":"70192086 - 2017 - Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch","interactions":[],"lastModifiedDate":"2017-10-19T15:19:36","indexId":"70192086","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch","docAbstract":"Evidence of climate-change-driven shifts in plant and animal phenology have raised concerns that certain trophic interactions may be increasingly mismatched in time, resulting in declines in reproductive success. Given the constraints imposed by extreme seasonality at high latitudes and the rapid shifts in phenology seen in the Arctic, we would also expect Antarctic species to be highly vulnerable to climate-change-driven phenological mismatches with their environment. However, few studies have assessed the impacts of phenological change in Antarctica. Using the largest database of phytoplankton phenology, sea-ice phenology, and Adélie Penguin breeding phenology and breeding success assembled to date, we find that, while a temporal match between Penguin breeding phenology and optimal environmental conditions sets an upper limit on breeding success, only a weak relationship to the mean exists. Despite previous work suggesting that divergent trends in Adélie Penguin breeding phenology are apparent across the Antarctic continent, we find no such trends. Furthermore, we find no trend in the magnitude of phenological mismatch, suggesting that mismatch is driven by interannual variability in environmental conditions rather than climate-change-driven trends, as observed in other systems. We propose several criteria necessary for a species to experience a strong climate-change-driven phenological mismatch, of which several may be violated by this system.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1749","usgsCitation":"Youngflesh, C., Jenouvrier, S., Li, Y., Ji, R., Ainley, D.G., Ballard, G., Barbraud, C., Delord, K., Dugger, K., Emmerson, L.M., Fraser, W.R., Hinke, J.T., Lyver, P.O., Olmastroni, S., Southwell, C.J., Trivelpiece, S.G., Trivelpiece, W.Z., and Lynch, H.J., 2017, Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch: Ecology, v. 98, no. 4, p. 940-951, https://doi.org/10.1002/ecy.1749.","productDescription":"12 p.","startPage":"940","endPage":"951","ipdsId":"IP-076568","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469974,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/8890","text":"External Repository"},{"id":346997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica","volume":"98","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-20","publicationStatus":"PW","scienceBaseUri":"59e9b995e4b05fe04cd65c97","contributors":{"authors":[{"text":"Youngflesh, Casey","contributorId":197697,"corporation":false,"usgs":false,"family":"Youngflesh","given":"Casey","email":"","affiliations":[],"preferred":false,"id":714111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenouvrier, Stephanie","contributorId":197698,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stephanie","email":"","affiliations":[],"preferred":false,"id":714112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Yun","contributorId":197732,"corporation":false,"usgs":false,"family":"Li","given":"Yun","affiliations":[],"preferred":false,"id":714113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ji, Rubao","contributorId":197699,"corporation":false,"usgs":false,"family":"Ji","given":"Rubao","affiliations":[],"preferred":false,"id":714114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ainley, David G.","contributorId":32039,"corporation":false,"usgs":false,"family":"Ainley","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":34154,"text":"Point Reyes Bird Observatory, Stinson Beach, CA","active":true,"usgs":false}],"preferred":false,"id":714115,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ballard, Grant","contributorId":197700,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","email":"","affiliations":[],"preferred":false,"id":714116,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barbraud, Christophe","contributorId":197701,"corporation":false,"usgs":false,"family":"Barbraud","given":"Christophe","email":"","affiliations":[],"preferred":false,"id":714117,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delord, Karine 0000-0001-6720-951X","orcid":"https://orcid.org/0000-0001-6720-951X","contributorId":197702,"corporation":false,"usgs":false,"family":"Delord","given":"Karine","email":"","affiliations":[],"preferred":false,"id":714118,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714110,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Emmerson, Loiuse M.","contributorId":197703,"corporation":false,"usgs":false,"family":"Emmerson","given":"Loiuse","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":714119,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fraser, William R.","contributorId":197704,"corporation":false,"usgs":false,"family":"Fraser","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":714120,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hinke, Jefferson T.","contributorId":197705,"corporation":false,"usgs":false,"family":"Hinke","given":"Jefferson","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":714121,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lyver, Phil O’B.","contributorId":197706,"corporation":false,"usgs":false,"family":"Lyver","given":"Phil","email":"","middleInitial":"O’B.","affiliations":[],"preferred":false,"id":714122,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Olmastroni, Silvia","contributorId":197707,"corporation":false,"usgs":false,"family":"Olmastroni","given":"Silvia","email":"","affiliations":[],"preferred":false,"id":714123,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Southwell, Colin J.","contributorId":197708,"corporation":false,"usgs":false,"family":"Southwell","given":"Colin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":714124,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Trivelpiece, Susan G.","contributorId":197709,"corporation":false,"usgs":false,"family":"Trivelpiece","given":"Susan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":714125,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Trivelpiece, Wayne Z.","contributorId":197710,"corporation":false,"usgs":false,"family":"Trivelpiece","given":"Wayne","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":714126,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Lynch, Heather J.","contributorId":197711,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":714127,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70183250,"text":"sir20175016 - 2017 - Occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir, Kansas, 2001-16","interactions":[],"lastModifiedDate":"2025-07-24T13:03:34.594363","indexId":"sir20175016","displayToPublicDate":"2017-03-31T11:15:00","publicationYear":"2017","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":"2017-5016","title":"Occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir, Kansas, 2001-16","docAbstract":"Cheney Reservoir, located in south-central Kansas, is one of the primary drinking-water supplies for the city of Wichita and an important recreational resource. Since 1990, cyanobacterial blooms have been present occasionally in Cheney Reservoir, resulting in increased treatment costs and decreased recreational use. Cyanobacteria, the cyanotoxin microcystin, and the taste-and-odor compounds geosmin and 2-methylisoborneol have been measured in Cheney Reservoir by the U.S. Geological Survey, in cooperation with the city of Wichita, for about 16 years. The purpose of this report is to describe the occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir during May 2001 through June 2016 and to update previously published logistic regression models that used continuous water-quality data to estimate the probability of microcystin and geosmin occurrence above relevant thresholds.
Cyanobacteria, microcystin, and geosmin were detected in about 84, 52, and 31 percent of samples collected in Cheney Reservoir during May 2001 through June 2016, respectively. 2-methylisoborneol was less common, detected in only 3 percent of samples. Microcystin and geosmin concentrations exceeded advisory values of concern more frequently than cyanobacterial abundance; therefore, cyanobacteria are not a good indicator of the presence of these taste-and-odor compounds in Cheney Reservoir. Broad seasonal patterns in cyanobacteria and microcystin were evident, though abundance and concentration varied by orders of magnitude across years. Cyanobacterial abundances generally peaked in late summer or early fall (August through October), and smaller peaks were observed in winter (January through February). In a typical year, microcystin was first detected in June or July, increased to its seasonal maxima in the summer (July through September), and then decreased. Seasonal patterns in geosmin were less consistent than cyanobacteria and microcystin, but geosmin typically had a small peak during winter (January through March) during most years and a large peak during summer (July through September) during some years. Though the relation between cyanobacterial abundance and microcystin and geosmin concentrations was positive, overall correlations were weak, likely because production is strain-specific and cyanobacterial strain composition may vary substantially over time. Microcystin often was present without taste-and-odor compounds. By comparison, where taste-and-odor compounds were present, microcystin frequently was detected. Taste-and-odor compounds, therefore, may be used as indicators that microcystin may be present; however, microcystin was present without taste-and-odor compounds, so taste or odor alone does not provide sufficient warning to ensure human-health protection.
Logistic regression models that estimate the probability of microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter and geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter were developed. Models were developed using the complete dataset (January 2003 through June 2016 for microcystin [14-year dataset]; May 2001 through June 2016 for geosmin [16-year dataset]) and an abbreviated 4-year dataset (January 2013 through June 2016 for microcystin and geosmin). Performance of the newly developed models was compared with previously published models that were developed using data collected during May 2001 through December 2009. A seasonal component and chlorophyll fluorescence (a surrogate for algal biomass) were the explanatory variables for microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in all models. All models were relatively robust, though the previously published and 14-year models performed better over time; however, as a tool to estimate microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in a real-time notification system near the Cheney Dam, the 4-year model is most representative of recent (2013 through 2016) conditions. All models for geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter had different explanatory variables and model forms. The previously published and 16-year models were not robust over time, likely because of changing environmental conditions and seasonal patterns in geosmin occurrence. By comparison, the abbreviated 4-year model may be a useful tool to estimate geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter in a real-time notification system near the Cheney Dam. The better performance of the abbreviated 4-year geosmin model during 2013 through 2016 relative to the previously published and 16-year models demonstrates the need for continuous reevaluation of models estimating the probability of occurrence.
Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049