Wetland indicator status (WIS) describes the habitat affinity of plant species and is used in wetland delineations and resource inventories. Understanding how species-level functional traits vary across WIS categories may improve designations, elucidate mechanisms of adaptation, and explain habitat optima and niche. We investigated differences in species-level traits of riparian flora across WIS categories, extending their application to indicate hydrologic habitat. We measured or compiled data on specific leaf area (SLA), stem specific gravity (SSG), seed mass, and mature height of 110 plant species that occur along the Colorado River in Grand Canyon, Arizona. Additionally, we measured leaf δ13C, δ15N, % carbon, % nitrogen, and C/N ratio of 56 species with C3 photosynthesis. We asked the following: (i) How do species-level traits vary over WIS categories? (ii) Does the pattern differ between herbaceous and woody species? (iii) How well do multivariate traits define WIS categories? (iv) Which traits are correlated? The largest trait differences among WIS categories for herbaceous species occurred for SSG, seed mass, % leaf carbon and height, and for woody species occurred for height, SSG, and δ13C. SSG increased and height decreased with habitat aridity for both woody and herbaceous species. The δ13C and hence water use efficiency of woody species increased with habitat aridity. Water use efficiency of herbaceous species increased with habitat aridity via greater occurrence of C4 grasses. Multivariate trait assemblages differed among WIS categories. Over all species, SLA was correlated with height, δ13C, % leaf N, and C/N; height was correlated with SSG and % leaf C; SSG was correlated with % leaf C. Adaptations of both herbaceous and woody riparian species to wet, frequently inundated habitats include low-density stem tissue. Adaptations to drier habitats in the riparian zone include short, high-density cavitation-resistant stem tissue, and high water use efficiency. The results enhance understanding about using traits to describe plant habitat in riparian systems.
","language":"English","publisher":"Blackwell Pub. Ltd","doi":"10.1002/ece3.2975","usgsCitation":"McCoy-Sulentic, M.E., Kolb, T.E., Merritt, D.M., Palmquist, E.C., Ralston, B.E., and Sarr, D.A., 2017, Variation in species-level plant functional traits over wetland indicator status categories: Ecology and Evolution, v. 7, no. 11, p. 3732-3744, https://doi.org/10.1002/ece3.2975.","productDescription":"13 p.","startPage":"3732","endPage":"3744","ipdsId":"IP-083993","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469833,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2975","text":"Publisher Index Page"},{"id":438333,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BV7DTQ","text":"USGS data release","linkHelpText":"Plant functional traits, Colorado River, Grand Canyon, 2012-2014Data"},{"id":341617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon, Marble Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.4503173828125,\n 35.73090666520053\n ],\n [\n -111.53045654296874,\n 35.73090666520053\n ],\n [\n -111.53045654296874,\n 36.90597988519294\n ],\n [\n -113.4503173828125,\n 36.90597988519294\n ],\n [\n -113.4503173828125,\n 35.73090666520053\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-17","publicationStatus":"PW","scienceBaseUri":"59269bb5e4b0b7ff9fb48963","contributors":{"authors":[{"text":"McCoy-Sulentic, Miles E.","contributorId":192228,"corporation":false,"usgs":false,"family":"McCoy-Sulentic","given":"Miles","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":695858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolb, Thomas E.","contributorId":189073,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":695859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merritt, David M.","contributorId":192229,"corporation":false,"usgs":false,"family":"Merritt","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":24595,"text":"USDA Forest Service, Fort Collins CO","active":true,"usgs":false}],"preferred":false,"id":695860,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":695857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ralston, Barbara E. 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":606,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","middleInitial":"E.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":false,"id":695861,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sarr, Daniel A. dsarr@usgs.gov","contributorId":191593,"corporation":false,"usgs":false,"family":"Sarr","given":"Daniel","email":"dsarr@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":false,"id":695862,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187170,"text":"ofr20171045 - 2017 - Oregon OCS seafloor mapping: Selected lease blocks relevant to renewable energy","interactions":[],"lastModifiedDate":"2017-06-23T12:33:29","indexId":"ofr20171045","displayToPublicDate":"2017-05-23T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1045","title":"Oregon OCS seafloor mapping: Selected lease blocks relevant to renewable energy","docAbstract":"In 2014 the U.S. Geological Survey (USGS) and the Bureau of Ocean Energy Management (BOEM) entered into Intra-agency agreement M13PG00037 to map an area of the Oregon Outer Continental Shelf (OCS) off of Coos Bay, Oregon, under consideration for development of a floating wind energy farm. The BOEM requires seafloor mapping and site characterization studies in order to evaluate the impact of seafloor and sub-seafloor conditions on the installation, operation, and structural integrity of proposed renewable energy projects, as well as to assess the potential effects of construction and operations on archaeological resources. The mission of the USGS is to provide geologic, topographic, and hydrologic information that contributes to the wise management of the Nation's natural resources and that promotes the health, safety, and well being of the people. This information consists of maps, databases, and descriptions and analyses of the water, energy, and mineral resources, land surface, underlying geologic structure, and dynamic processes of the earth.
For the Oregon OCS study, the USGS acquired multibeam echo sounder and seafloor video data surrounding the proposed development site, which is 95 km2 in area and 15 miles offshore from Coos Bay. The development site had been surveyed by Solmar Hydro Inc. in 2013 under a contract with WindFloat Pacific. The USGS subsequently produced a bathymetry digital elevation model and a backscatter intensity grid that were merged with existing data collected by the contractor. The merged grids were published along with visual observations of benthic geo-habitat from the video data in an associated USGS data release (Cochrane and others, 2015).
This report includes the results of analysis of the video data conducted by Oregon State University and the geo-habitat interpretation of the multibeam echo sounder (MBES) data conducted by the USGS. MBES data was published in Cochrane and others (2015). Interpretive data associated with this publication is published in Cochrane (2017). All the data is provided as geographic information system (GIS) files that contain both Esri ArcGIS geotiffs or shapefiles. For those who do not own the full suite of Esri GIS and mapping software, the data can be read using Esri ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed August 29, 2016). Web services, which consist of standard implementations of ArcGIS representational state transfer (REST) Service and Open Geospatial Consortium (OGC) GIS web map service (WMS), also are available for all published GIS data. Web services were created using an ArcGIS service definition file, resulting in data layers that are symbolized as shown on the associated report figures. Both the ArcGIS REST Service and OGC WMS Service include all the individual GIS layers. Data layers are bundled together in a map-area web service; however, each layer can be symbolized and accessed individually after the web service is ingested into a desktop application or web map. Web services enable users to download and view data, as well as to easily add data to their own workflows, using any browser-enabled, standalone or mobile device.
Though the surficial substrate is dominated by combinations of mud and sand substrate, a diverse assortment of geomorphologic features are related to geologic processes—one anticlinal ridge where bedrock is exposed, a slump and associated scarps, and pockmarks. Pockmarks are seen in the form of fields of small pockmarks, a lineation of large pockmarks with methanogenic carbonates, and areas of large pockmarks that have merged into larger variously shaped depressions. The slump appears to have originated at the pockmark lineation. Video-supervised numerical analysis of the MBES backscatter intensity data and vector ruggedness derived from the MBES bathymetry data was used to produce a substrate model called a seafloor character raster for the study area. The seafloor character raster consists of three substrate classes: soft-flat areas, hard-flat areas, and hard-rugged areas. A Coastal and Marine Ecological Classification Standard (CMECS) geoform and substrate map was also produced using depth, slope, and benthic position index classes to delineate geoform boundaries. Seven geoforms were identified in this process, including ridges, slump scars, slump deposits, basins, and pockmarks.
Statistical analysis of the video data for correlations between substrate, depth, and invertebrate assemblages resulted in the identification of seven biomes: three hard-bottom biomes and four softbottom biomes. A similar analysis of vertebrate observations produces a similar set of biomes. The biome between-group dissimilarity was very high or high. Invertebrates alone represent most of the structure of the whole benthic community into different assemblages. A biotope map was generated using the seafloor character raster and the substrate and depth values of the biomes. Hard substrate biotopes were small in size and were located primarily on the ridge and in pockmarks along the pockmark lineation. The soft-bottom bitopes consisted of large contiguous areas delimited by isobaths.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171045","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management","usgsCitation":"Cochrane, G.R., Hemery, L.G., and Henkel, S.K., 2017, Oregon OCS seafloor mapping: Selected lease blocks relevant to renewable energy: U.S. Geological Survey Open-File Report 2017-1045 and Bureau of Ocean Energy Management OCS Study BOEM 2017-018, 51 p., https://doi.org/10.3133/ofr20171045.","productDescription":"v, 51 p.","numberOfPages":"57","onlineOnly":"Y","ipdsId":"IP-080496","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438336,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7000069","text":"USGS data release","linkHelpText":"Interpretive data release for Oregon OCS Seafloor Mapping: Selected Lease Blocks Relevant to Renewable Energy"},{"id":341588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1045/ofr20171045.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1045"},{"id":341585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1045/coverthb.jpg"}],"contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
In 2009, the Federal Highway Administration published Hydraulic Engineering Circular No. 23 (HEC-23) to provide specific design and implementation guidelines for bridge scour and stream instability countermeasures. However, the effectiveness of countermeasures implemented over the past decade following those guidelines has not been evaluated. Therefore, in 2013, the U.S. Geological Survey, in cooperation with the Federal Highway Administration, began a study to assess the current condition of bridge-scour countermeasures at selected sites to evaluate their effectiveness. Bridge-scour countermeasures were assessed during 2014-2016. Site assessments included reviewing countermeasure design plans, summarizing the peak and daily streamflow history, and assessments at each site. Each site survey included a photo log summary, field form, and topographic and bathymetric geospatial data and metadata. This report documents the study area and site-selection criteria, explains the survey methods used to evaluate the condition of countermeasures, and presents the complete documentation for each countermeasure assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171048","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Dudunake, T.J., Huizinga , R.J., and Fosness, R.L., 2017, Bridge scour countermeasure assessments at select bridges in the United States, 2014–16 (ver. 1.1, October 2017): U.S. Geological Survey Open-File Report 2017-1048, 10 p., https://doi.org/10.3133/ofr20171048.","productDescription":"Report: iv, 10 p.; Table 3: HTML Document; 13 Additional Report Pieces: zip files; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074991","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":341477,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71R6NQ2","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Data for Bridge Scour Countermeasure 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\"}}]}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Differences in plant communities are a response to the abiotic environment, species interactions, and dispersal. The role of geographic distance relative to the abiotic environment is explored for alpine tundra vegetation from 319 plots of four regions along the Rocky Mountain cordillera in the USA. The site by species data were ordinated using nonmetric multidimensional scaling to produce dependent variables for use in best-subsets regression. For independent variables, observations of local topography and microtopography were used as environmental indicators. Two methods of including distance in studies of vegetation and environment are used and contrasted. The relative importance of geographic distance in accounting for the pattern of alpine tundra similarity indicates that location is a factor in plant community composition. Mantel tests provide direct correlations between difference and distance but have known weaknesses. Moran spatial eigenvectors used in regression based approaches have greater geographic specificity, but require another step, ordination, in creating a vegetation variable. While the spatial eigenvectors are generally preferable, where species–environment relations are weak, as seems to be the case for the alpine sites studied here, the fewer abstractions of the Mantel test may be useful.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02723646.2017.1327284","collaboration":"George Malanson; Dale Zimmerman","usgsCitation":"Malanson, G.P., Zimmerman, D.L., and Fagre, D.B., 2017, Distance and environmental difference in alpine plant communities: Physical Geography, v. 38, no. 6, p. 489-505, https://doi.org/10.1080/02723646.2017.1327284.","productDescription":"16 p.","startPage":"489","endPage":"505","ipdsId":"IP-071595","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":346464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Rocky Mountain cordillera","volume":"38","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-22","publicationStatus":"PW","scienceBaseUri":"59defc13e4b05fe04ccd3d5a","contributors":{"authors":[{"text":"Malanson, George P.","contributorId":189162,"corporation":false,"usgs":false,"family":"Malanson","given":"George","email":"","middleInitial":"P.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":712086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Dale L.","contributorId":166811,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Dale","email":"","middleInitial":"L.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":712085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":712084,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187851,"text":"70187851 - 2017 - Host density increases parasite recruitment but decreases host risk in a snail-trematode system","interactions":[],"lastModifiedDate":"2017-08-03T08:29:47","indexId":"70187851","displayToPublicDate":"2017-05-22T00:00:00","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":"Host density increases parasite recruitment but decreases host risk in a snail-trematode system","docAbstract":"Most species aggregate in local patches. High host density in patches increases contact rate between hosts and parasites, increasing parasite transmission success. At the same time, for environmentally-transmitted parasites, high host density can decrease infection risk to individual hosts, because infective stages are divided among all hosts in a patch, leading to safety in numbers. We tested these predictions using the California horn snail, Cerithideopsis californica (=Cerithidea californica), which is the first intermediate host for at least 19 digenean trematode species in California estuaries. Snails become infected by ingesting trematode eggs or through penetration by free-swimming miracidia that hatch from trematode eggs deposited with final-host (bird or mammal) feces. This complex life cycle decouples infective-stage production from transmission, raising the possibility of an inverse relationship between host density and infection risk. In a field survey, higher snail density was associated with increased trematode (infected snail) density, but decreased trematode prevalence, consistent with either safety in numbers, parasitic castration, or both. To determine the extent to which safety in numbers drove the negative snail density-trematode prevalence association, we manipulated uninfected snail density in 83 cages at eight sites within Carpinteria Salt Marsh (CA, USA). At each site, we quantified snail density and used data on final-host (bird and raccoon) distributions to control for between-site variation in infective-stage supply. After three months, overall trematode infections per cage increased with snail-biomass density. For egg-transmitted trematodes, per-snail infection risk decreased with snail-biomass density in the cage and surrounding area, whereas per-snail infection risk did not decrease for miracidium-transmitted trematodes. Furthermore, both trematode recruitment and infection risk increased with infective-stage input, but this was significant only for miracidium-transmitted species. A model parameterized with our experimental results and snail densities from 524 field transects estimated that safety in numbers, when combined with host aggregation, halved per-capita infection risk in this snail population. We conclude that, depending on transmission mode, host density can enhance parasite recruitment and reduce per-capita infection risk.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1905","usgsCitation":"Buck, J.C., Hechinger, R., Wood, A., Stewart, T., Kuris, A., and Lafferty, K.D., 2017, Host density increases parasite recruitment but decreases host risk in a snail-trematode system: Ecology, v. 98, no. 8, p. 2029-2038, https://doi.org/10.1002/ecy.1905.","productDescription":"10 p.","startPage":"2029","endPage":"2038","ipdsId":"IP-076667","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":438338,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GX48P2","text":"USGS data release","linkHelpText":"Host Influence and Risk of Parasite Recruitment in a Snail-Trematode System at Carpinteria Salt Marsh, 2012-2015 Field Experiment"},{"id":341553,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"8","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-06","publicationStatus":"PW","scienceBaseUri":"5923f8dfe4b0b7ff9fb23412","contributors":{"authors":[{"text":"Buck, Julia C","contributorId":192180,"corporation":false,"usgs":false,"family":"Buck","given":"Julia","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":695740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hechinger, R.F.","contributorId":192181,"corporation":false,"usgs":false,"family":"Hechinger","given":"R.F.","email":"","affiliations":[],"preferred":false,"id":695741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, A.C.","contributorId":192182,"corporation":false,"usgs":false,"family":"Wood","given":"A.C.","email":"","affiliations":[],"preferred":false,"id":695742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, T.E.","contributorId":192183,"corporation":false,"usgs":false,"family":"Stewart","given":"T.E.","email":"","affiliations":[],"preferred":false,"id":695743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuris, A.M.","contributorId":192184,"corporation":false,"usgs":false,"family":"Kuris","given":"A.M.","email":"","affiliations":[],"preferred":false,"id":695744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":695739,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70185152,"text":"ofr20171033 - 2017 - U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2016","interactions":[],"lastModifiedDate":"2018-04-24T13:39:49","indexId":"ofr20171033","displayToPublicDate":"2017-05-19T10:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1033","title":"U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2016","docAbstract":"2016 was an exciting year for the Department of the Interior (DOI) Climate Science Centers (CSCs) and the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC). In recognition of our ongoing efforts to raise awareness and provide the scientific data and tools needed to address the impacts of climate change on fish, wildlife, ecosystems, and people, NCCWSC and the CSCs received an honorable mention in the first ever Climate Adaptation Leadership Award for Natural Resources sponsored by the National Fish, Wildlife, and Plant Climate Adaptation Strategy’s Joint Implementation Working Group. The recognition is a reflection of our contribution to numerous scientific workshops and publications, provision of training for students and early career professionals, and work with Tribes and indigenous communities to improve climate change resilience across the Nation. In this report, we highlight some of the activities that took place throughout the NCCWSC and CSC network in 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171033","usgsCitation":"Weiskopf, S.R., Varela Minder, Elda, and Padgett, H.A., 2017, U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2016: U.S. Geological Survey Open-File Report 2017–1033, 12 p., https://doi.org/10.3133/ofr20171033.","productDescription":"12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-080704","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science 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States\"}}]}","contact":"Director, National Climate Change and Wildlife Science Center (NCCWSC)
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 516
Reston, VA 20192
In Dixie Valley, Nevada, an isolated population of toads has been the subject of proactive conservation measures by the Nevada Department of Wildlife and the U.S. Fish and Wildlife Service since 2008 due to concerns about potential habitat degradation resulting from exploitation of nearby geothermal energy resources. These toads appear to belong within the Anaxyrus boreas species group but are commonly referred to as Dixie Valley toads (DVTs). The DVT is currently confined to an extremely narrow habitat range (370 ha) that is geographically isolated from any other A. boreas population. In this study, genetic variations in mitochondrial genes and 11 microsatellite loci were used to assess the affinities of DVTs in relation to members of the A. boreas species group. We compared results from DVTs with previously published data spanning much of the range of A. boreas in the United States and new data from a nearby toad population within Dixie Valley. Data from both mitochondrial DNA and microsatellites placed DVTs inside the A. boreas species group. In particular, DVTs fell into a cluster of A. boreas from Washington and California, along with other species from the A. boreas species group, namely A. nelsoni, A. canorus, and A. exsul. Genetic differentiation of DVTs was lowest between A. boreas populations in Washington and California. However, allele frequencies were significantly different between DVTs and all other populations, including a nearby locality within Dixie Valley. This genetic differentiation, along with the DVT's geographical isolation and restricted habitat, warrants recognition of the DVT as a distinct management unit.
","language":"English","publisher":"BioOne","doi":"10.3398/064.077.0204","usgsCitation":"Forrest, M.J., Stiller, J., King, T.L., and Rouse, G., 2017, Between hot rocks and dry places: The status of the Dixie Valley toad: Western North American Naturalist, v. 77, no. 2, p. 162-175, https://doi.org/10.3398/064.077.0204.","productDescription":"14 p.","startPage":"162","endPage":"175","ipdsId":"IP-082373","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":488138,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsarchive.byu.edu/wnan/vol77/iss2/3","text":"External Repository"},{"id":376951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Dixie Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.19366455078125,\n 39.42346418978382\n ],\n [\n -117.94097900390625,\n 39.57605638518604\n ],\n [\n -117.55920410156249,\n 40.13269100586688\n ],\n [\n -117.58392333984375,\n 40.373751366720505\n ],\n [\n -117.6910400390625,\n 40.386304853509046\n ],\n [\n -117.79541015625001,\n 40.25437660372649\n ],\n [\n -117.88330078125,\n 40.000267972646796\n ],\n [\n -118.20465087890625,\n 39.75576851405812\n ],\n [\n -118.25408935546875,\n 39.55700068337126\n ],\n [\n -118.30352783203125,\n 39.49556336059472\n ],\n [\n -118.21563720703124,\n 39.42134249546523\n ],\n [\n -118.19366455078125,\n 39.42346418978382\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Forrest, Matthew J.","contributorId":8383,"corporation":false,"usgs":true,"family":"Forrest","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":794646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stiller, Josefin","contributorId":236901,"corporation":false,"usgs":false,"family":"Stiller","given":"Josefin","email":"","affiliations":[{"id":47561,"text":"Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA.","active":true,"usgs":false}],"preferred":false,"id":794647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouse, Greg","contributorId":169158,"corporation":false,"usgs":false,"family":"Rouse","given":"Greg","email":"","affiliations":[{"id":6728,"text":"Scripps Inst Oceanography","active":true,"usgs":false}],"preferred":false,"id":794648,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187478,"text":"ofr20171053 - 2017 - Community for Data Integration 2016 annual report","interactions":[],"lastModifiedDate":"2022-04-22T15:55:18.055181","indexId":"ofr20171053","displayToPublicDate":"2017-05-19T00:13:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1053","title":"Community for Data Integration 2016 annual report","docAbstract":"The Community for Data Integration (CDI) represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the U.S. Geological Survey and the CDI community. This annual report describes the various presentations, activities, and outcomes of the CDI monthly forums, working groups, virtual training series, and other CDI-sponsored events in fiscal year 2016. The report also describes the objectives and accomplishments of the 13 CDI-funded projects in fiscal year 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171053","usgsCitation":"Langseth, M.L., Hsu, Leslie, Amberg, Jon, Bliss, Norman, Bock, A.R., Bolus, R.T., Bristol, R.S., Chase, K.J., Crimmins, T.M., Earle, P.S., Erickson, Richard, Everette, A.L., Falgout, Jeff, Faundeen, J.L., Fienen, Michael, Griffin, Rusty, Guy, M.R., Henry, K.D., Hoebelheinrich, N.J., Hunt, Randall, Hutchison, V.B., Ignizio, D.A., Infante, D.M., Jarnevich, Catherine, Jones, J.M., Kern, Tim, Leibowitz, Scott, Lightsom, F.L., Marsh, R.L., McCalla, S.G., McNiff, Marcia, Morisette, J.T., Nelson, J.C., Norkin, Tamar, Preston, T.M., Rosemartin, Alyssa, Sando, Roy, Sherba, J.T., Signell, R.P., Sleeter, B.M., Sundquist, E.T., Talbert, C.B., Viger, R.J., Weltzin, J.F., Waltman, Sharon, Weber, Marc, Wieferich, D.J., Williams, Brad, Windham-Myers, Lisamarie, 2017, Community for Data Integration 2016 annual report: U.S. Geological Survey Open-File Report 2017–1053, 40 p., https://doi.org/10.3133/ofr20171053.","productDescription":"viii, 40 p.","onlineOnly":"Y","ipdsId":"IP-084040","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":341421,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1053/ofr20171053.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1053"},{"id":341420,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1053/coverthb.jpg"}],"contact":" Core Science Analytics and Synthesis
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive,
Reston, VA 20192
Genomic responses to habitat conversion can be rapid, providing wildlife managers with time-limited opportunities to enact recovery efforts that use population connectivity information that reflects predisturbance landscapes. Despite near-complete biome conversion, such opportunities may still exist for the endemic fauna and flora of California's San Joaquin Desert, but comprehensive genetic data sets are lacking for nearly all species in the region. To fill this knowledge gap, we studied the rangewide population structure of the endangered blunt-nosed leopard lizard Gambelia sila, a San Joaquin Desert endemic, using restriction site-associated DNA (RAD), microsatellite and mtDNA data to test whether admixture patterns and estimates of effective migration surfaces (EEMS) can identify land areas with high population connectivity prior to the conversion of native xeric habitats. Clustering and phylogenetic analyses indicate a recent shared history between numerous isolated populations and EEMS reveals latent signals of corridors and barriers to gene flow over areas now replaced by agriculture and urbanization. Conflicting histories between the mtDNA and nuclear genomes are consistent with hybridization with the sister species G. wislizenii, raising important questions about where legal protection should end at the southern range limit of G. sila. Comparative analysis of different data sets also adds to a growing list of advantages in using RAD loci for genetic studies of rare species. We demonstrate how the results of this work can serve as an evolutionary guidance tool for managing endemic, arid-adapted taxa in one of the world's most compromised landscapes.
","language":"English","publisher":"Wiley","doi":"10.1111/mec.14125","usgsCitation":"Richmond, J.Q., Wood, D.A., Westphal, M.F., Vandergast, A.G., Leache, A.D., Saslaw, L., Butterfield, H.S., and Fisher, R.N., 2017, Persistence of historical population structure in an endangered species despite near-complete biome conversion in California's San Joaquin Desert: Molecular Ecology, v. 26, no. 14, p. 3618-3635, https://doi.org/10.1111/mec.14125.","productDescription":"18 p.","startPage":"3618","endPage":"3635","ipdsId":"IP-076736","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":461579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mec.14125","text":"Publisher Index Page"},{"id":341498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.1517333984375,\n 34.619647359797185\n ],\n [\n -117.82287597656249,\n 34.619647359797185\n ],\n [\n -117.82287597656249,\n 37.01571219880126\n ],\n [\n -121.1517333984375,\n 37.01571219880126\n ],\n [\n -121.1517333984375,\n 34.619647359797185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"14","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-02","publicationStatus":"PW","scienceBaseUri":"5920044ae4b0ac16dbdeb784","contributors":{"authors":[{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":695609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":695610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Westphal, Michael F.","contributorId":192139,"corporation":false,"usgs":false,"family":"Westphal","given":"Michael","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":695611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":695612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leache, Adam D.","contributorId":192142,"corporation":false,"usgs":false,"family":"Leache","given":"Adam","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":695615,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saslaw, Lawrence","contributorId":192140,"corporation":false,"usgs":false,"family":"Saslaw","given":"Lawrence","email":"","affiliations":[],"preferred":false,"id":695613,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butterfield, H. Scott","contributorId":192141,"corporation":false,"usgs":false,"family":"Butterfield","given":"H.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":695614,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":695608,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70182251,"text":"ofr20171023 - 2017 - Estimates of immediate effects on world markets of a hypothetical disruption to Russia’s supply of six mineral commodities","interactions":[],"lastModifiedDate":"2017-05-18T12:55:15","indexId":"ofr20171023","displayToPublicDate":"2017-05-18T13:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1023","title":"Estimates of immediate effects on world markets of a hypothetical disruption to Russia’s supply of six mineral commodities","docAbstract":"The potential immediate effects of a hypothetical shock to Russia’s supply of selected mineral commodities on the world market and on individual countries were determined and monetized (in 2014 U.S. dollars). The mineral commodities considered were aluminum (refined primary), nickel (refined primary), palladium (refined) and platinum (refined), potash, and titanium (mill products), and the regions and countries of primary interest were the United States, the European Union (EU–28), and China. The shock is assumed to have infinite duration, but only the immediate effects, those limited by a 1-year period, are considered.
A methodology for computing and monetizing the potential impacts was developed. Then the data pertaining to all six mineral commodities were collected and the most likely effects were computed. Because of the uncertainties associated with some of the data, sensitivity analyses were conducted to confirm the validity of the results.
Results indicate that the impact on the United States arising from a shock to Russia’s supply, in terms of the value of net exports, would range from a gain of \\$336 million for titanium mill products to a loss of \\$237 million for potash; thus, the overall effect of a supply shock is likely to be quite modest. The study also demonstrates that, taken alone, Russia’s share in the world production of a particular commodity is not necessarily indicative of the size of potential impacts resulting from a supply shock; other factors, such as prices, domestic production, and the structure of international commodity flows were found to be important as well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171023","usgsCitation":"Safirova, Elena, Barry, J.J., Hastorun, Sinan, Matos, G.R., and Perez, A.A., with contributions from Bedinger, G.M., Bray, E.L., Jasinski, S.M., Kuck, P.H., and Loferski, P.J., 2017, Estimates of immediate effects on world markets of a hypothetical disruption to Russia’s supply of six mineral commodities: U.S. Geological Survey Open-File Report 2017–1023, 22 p., https://doi.org/10.3133/ofr20171023.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068616","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":340045,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1023/coverthb.jpg"},{"id":340046,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1023/ofr20171023.pdf","text":"Report","size":"390 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1023"}],"contact":"National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Remote sensing-based field-scale evapotranspiration (ET) maps are useful for characterizing water use patterns and assessing crop performance. The relative impact of climate variability and water management decisions are better studied and quantified using historical data that are derived using a set of consistent datasets and methodology. Historical (1984–2014) Landsat-based ET maps were generated for major irrigation districts in California, i.e., Palo Verde and eight other sub-basins in parts of the middle and lower Central Valley. A total of 3396 Landsat images were processed using the Operational Simplified Surface Energy Balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study sites over the 31 years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively good correspondence (R2 up to 0.88, root mean square error as low as 14 mm/month) between SSEBop ET and validation datasets. In a pairwise comparison, annual variability of agro-hydrologic parameters of actual evapotranspiration (ETa), land surface temperature (Ts), and runoff (Q) were found to be more variable than their corresponding climatic counterparts of atmospheric water demand (ETo), air temperature (Ta), and precipitation (P), revealing process differences between regional climatic drivers and localized agro-hydrologic responses. However, only Ta showed a consistent increase (up to 1.2 K) over study sites during the 31 years, whereas other climate variables such as ETo and P showed a generally neutral trend. This study demonstrates a useful application of “Big Data” science where large volumes of historical Landsat and weather datasets were used to quantify and understand the relative importance of water management and climate variability in crop water use dynamics in regards to the linkages among water management decisions, hydrologic processes and economic transactions. Irrigation district-wide ETa estimates were used to compute historical crop water use volumes and monetary equivalents of water savings for the Palo Verde Irrigation District (PVID). During the peak crop fallowing year in PVID, the water saved reached a maximum of ~ 107,200 acre-feet in 2011 with an estimated monetary payout value of $20.5 million. A significant decreasing trend in actual ET despite an increasing atmospheric demand in PVID highlights the role of management decisions in affecting local hydrologic processes. This study has importance for planning water resource allocation, managing water rights, sustaining agricultural production, and quantifying impacts of climate and land use/land cover changes on water resources. With increased computational efficiency, similar studies can be conducted in other parts of the world to help policy and decision makers understand and quantify various aspects of water resources management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.05.005","usgsCitation":"Senay, G.B., Schauer, M., Friedrichs, M., Velpuri, N., and Singh, R., 2017, Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States: Remote Sensing of Environment, v. 202, p. 98-112, https://doi.org/10.1016/j.rse.2017.05.005.","productDescription":"15 p.","startPage":"98","endPage":"112","ipdsId":"IP-084512","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493296,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.05.005","text":"Publisher Index Page"},{"id":493191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.40977836532282,\n 42.04933677309765\n ],\n [\n -123.16675091874134,\n 36.46673710104686\n ],\n [\n -118.58307879273526,\n 32.705501119947826\n ],\n [\n -113.73717874196483,\n 32.12914120007623\n ],\n [\n -113.88408063137007,\n 35.268837865873046\n ],\n [\n -114.8258516238531,\n 35.43740401775197\n ],\n [\n -117.48081678552326,\n 37.20400442230724\n ],\n [\n -120.03564230020528,\n 39.18700095919894\n ],\n [\n -120.18220374157768,\n 41.98136883976758\n ],\n [\n -125.40977836532282,\n 42.04933677309765\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"202","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":216909,"corporation":false,"usgs":true,"family":"Schauer","given":"Matthew","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Ramesh 0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":210983,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187773,"text":"70187773 - 2017 - Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.","interactions":[],"lastModifiedDate":"2017-05-18T12:57:32","indexId":"70187773","displayToPublicDate":"2017-05-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.","docAbstract":"A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature (Ts) normalized difference vegetation index (NDVI) and daily maximum air temperature (Ta). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using Ts and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the Ts from Landsat relative to the Ts from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.
The accuracy of global positioning system (GPS) locations obtained from study animals tagged with GPS monitoring devices has been a concern as to the degree it influences assessments of movement patterns, space use, and resource selection estimates. Many methods have been proposed for screening data to retain the most accurate positions for analysis, based on dilution of precision (DOP) measures, and whether the position is a two dimensional or three dimensional fix. Here we further explore the utility of these measures, by testing a Telonics GEN3 GPS collar's positional accuracy across a wide range of environmental conditions. We found the relationship between location error and fix dimension and DOP metrics extremely weak (r2adj ∼ 0.01) in our study area. Environmental factors such as topographic exposure, canopy cover, and vegetation height explained more of the variance (r2adj = 15.08%). Our field testing covered sites where sky-view was so limited it affected GPS performance to the degree fix attempts failed frequently (fix success rates ranged 0.00–100.00% over 67 sites). Screening data using PDOP did not effectively reduce the location error in the remaining dataset. Removing two dimensional fixes reduced the mean location error by 10.95 meters, but also resulted in a 54.50% data reduction. Therefore screening data under the range of conditions sampled here would reduce information on animal movement with minor improvements in accuracy and potentially introduce bias towards more open terrain and vegetation.
","language":"English","publisher":"Nordic Board for Wildlife Research","doi":"10.2981/wlb.00229","usgsCitation":"Ironside, K.E., Mattson, D.J., Arundel, T.R., and Hansen, J.R., 2017, Is GPS telemetry location error screening beneficial?: Wildlife Biology, wlb.00229: 7 p., https://doi.org/10.2981/wlb.00229.","productDescription":"wlb.00229: 7 p.","ipdsId":"IP-072502","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469839,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00229","text":"Publisher Index Page"},{"id":342592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5944ee14e4b062508e3335f8","contributors":{"authors":[{"text":"Ironside, Kirsten E. 0000-0003-1166-3793 kironside@usgs.gov","orcid":"https://orcid.org/0000-0003-1166-3793","contributorId":3379,"corporation":false,"usgs":true,"family":"Ironside","given":"Kirsten","email":"kironside@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mattson, David J.","contributorId":191920,"corporation":false,"usgs":false,"family":"Mattson","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":698453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arundel, Terence R. 0000-0003-0324-4249 tarundel@usgs.gov","orcid":"https://orcid.org/0000-0003-0324-4249","contributorId":139242,"corporation":false,"usgs":true,"family":"Arundel","given":"Terence","email":"tarundel@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Jered R. jrhansen@usgs.gov","contributorId":5161,"corporation":false,"usgs":true,"family":"Hansen","given":"Jered","email":"jrhansen@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698455,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187787,"text":"70187787 - 2017 - A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders","interactions":[],"lastModifiedDate":"2017-12-21T10:19:21","indexId":"70187787","displayToPublicDate":"2017-05-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders","docAbstract":"Several spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.
We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.
Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.
Declines in bird populations in agricultural regions of North America and Europe have been attributed to agricultural industrialization, increases in use of agrochemical application, and increased predation related to habitat modification. Based on count data compiled from Breeding Bird Survey (BBS) from 1974 to 2012, Christmas Bird Count (CBC) collected from 1914 to 2013, and hunter data from Annual Game Take Survey (AGTS) for years 1948–2010, ring-necked pheasants (Phasianus colchicus) in California have experienced substantial declines in agricultural environments. Using a modeling approach that integrates all three forms of survey data into a joint response abundance index, we found pheasant abundance was related to the amount of harvested and unharvested crop land, types of crops produced, amount of total pesticide applied, minimum temperature, precipitation, and numbers of avian competitors and predators. Specifically, major changes in agricultural practices over the last three decades were associated with declines in pheasant numbers and likely reflected widespread loss of habitat. For example, increases in cropland were associated with increased pheasant abundance during early years of study but this effect decreased through time, such that no association in recent years was evidenced. A post hoc analysis revealed that crops beneficial to pheasant abundance (e.g., barley) have declined substantially in recent decades and were replaced by less advantageous crops (e.g., nut trees). An additional analysis using a restricted data set (1990–2013) indicated recent negative impacts on pheasant numbers associated with land use practices were also associated with relatively high levels of pesticide application. Our results may provide valuable information for management policies aimed at reducing widespread declines in pheasant populations in California and may be applicable to other avian species within agricultural settings. Furthermore, this general analytical approach is not limited to pheasants and could be applied to other taxa for which multiple survey data sources exist.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2675","usgsCitation":"Coates, P.S., Brussee, B.E., Howe, K., Fleskes, J., Dwight, I., Connelly, D.P., Meshriy, M., and Gardner, S.C., 2017, Long-term and widespread changes in agricultural practices influence ring-necked pheasant abundance in California: Ecology and Evolution, v. 7, no. 8, p. 2546-2559, https://doi.org/10.1002/ece3.2675.","productDescription":"14 p.","startPage":"2546","endPage":"2559","ipdsId":"IP-075891","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":461589,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2675","text":"Publisher Index Page"},{"id":341391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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bears","interactions":[],"lastModifiedDate":"2018-04-21T13:18:43","indexId":"70187710","displayToPublicDate":"2017-05-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Habitat degradation affects the summer activity of polar bears","docAbstract":"Understanding behavioral responses of species to environmental change is critical to forecasting population-level effects. Although climate change is significantly impacting species’ distributions, few studies have examined associated changes in behavior. Polar bear (Ursus maritimus) subpopulations have varied in their near-term responses to sea ice decline. We examined behavioral responses of two adjacent subpopulations to changes in habitat availability during the annual sea ice minimum using activity data. Location and activity sensor data collected from 1989 to 2014 for 202 adult female polar bears in the Southern Beaufort Sea (SB) and Chukchi Sea (CS) subpopulations were used to compare activity in three habitat types varying in prey availability: (1) land; (2) ice over shallow, biologically productive waters; and (3) ice over deeper, less productive waters. Bears varied activity across and within habitats with the highest activity at 50–75% sea ice concentration over shallow waters. On land, SB bears exhibited variable but relatively high activity associated with the use of subsistence-harvested bowhead whale carcasses, whereas CS bears exhibited low activity consistent with minimal feeding. Both subpopulations had fewer observations in their preferred shallow-water sea ice habitats in recent years, corresponding with declines in availability of this substrate. The substantially higher use of marginal habitats by SB bears is an additional mechanism potentially explaining why this subpopulation has experienced negative effects of sea ice loss compared to the still-productive CS subpopulation. Variability in activity among, and within, habitats suggests that bears alter their behavior in response to habitat conditions, presumably in an attempt to balance prey availability with energy costs.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-017-3839-y","usgsCitation":"Ware, J.V., Rode, K.D., Bromaghin, J.F., Douglas, D.C., Wilson, R.H., Regehr, E.V., Amstrup, S.C., Durner, G.M., Pagano, A.M., Olson, J., Robbins, C.T., and Jansen, H.T., 2017, Habitat degradation affects the summer activity of polar bears: Oecologia, v. 184, no. 1, p. 87-99, https://doi.org/10.1007/s00442-017-3839-y.","productDescription":"13 p.","startPage":"87","endPage":"99","ipdsId":"IP-073535","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":438339,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7B27SCH","text":"USGS data release","linkHelpText":"Summer Activity Sensor Data from Collars Deployed on Female Polar Bears in the Chukchi Sea 1989 to 1995 and Southern Beaufort Sea 1989 to 2014"},{"id":341342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Chukchi Sea, Southern Beaufort Sea","volume":"184","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-28","publicationStatus":"PW","scienceBaseUri":"591c0fc8e4b0a7fdb43ddeea","contributors":{"authors":[{"text":"Ware, Jasmine V.","contributorId":192039,"corporation":false,"usgs":false,"family":"Ware","given":"Jasmine","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":695205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":695204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":695206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":695207,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Ryan H. 0000-0001-7740-7771","orcid":"https://orcid.org/0000-0001-7740-7771","contributorId":130989,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":695208,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":695209,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amstrup, Steven C.","contributorId":67034,"corporation":false,"usgs":false,"family":"Amstrup","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":13182,"text":"Polar Bears International","active":true,"usgs":false}],"preferred":false,"id":695210,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":695211,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":695212,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Olson, Jay","contributorId":150116,"corporation":false,"usgs":false,"family":"Olson","given":"Jay","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":695213,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robbins, Charles T.","contributorId":124585,"corporation":false,"usgs":false,"family":"Robbins","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":5127,"text":"Washington State University, P.O. Box 644236, Pullman, WA 99164","active":true,"usgs":false}],"preferred":false,"id":695214,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jansen, Heiko T","contributorId":192040,"corporation":false,"usgs":false,"family":"Jansen","given":"Heiko","email":"","middleInitial":"T","affiliations":[],"preferred":false,"id":695215,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70193711,"text":"70193711 - 2017 - Designing a global assessment of climate change on inland fishes and fisheries: knowns and needs","interactions":[],"lastModifiedDate":"2017-11-08T15:06:46","indexId":"70193711","displayToPublicDate":"2017-05-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Designing a global assessment of climate change on inland fishes and fisheries: knowns and needs","docAbstract":"To date, there are few comprehensive assessments of how climate change affects inland finfish, fisheries, and aquaculture at a global scale, but one is necessary to identify research needs and commonalities across regions and to help guide decision making and funding priorities. Broadly, the consequences of climate change on inland fishes will impact global food security, the livelihoods of people who depend on inland capture and recreational fisheries. However, understanding how climate change will affect inland fishes and fisheries has lagged behind marine assessments. Building from a North American inland fisheries assessment, we convened an expert panel from seven countries to provide a first-step to a framework for determining how to approach an assessment of how climate change may affect inland fishes, capture fisheries, and aquaculture globally. Starting with the small group helped frame the key questions (e.g., who is the audience? What is the best approach and spatial scale?). Data gaps identified by the group include: the tolerances of inland fisheries to changes in temperature, stream flows, salinity, and other environmental factors linked to climate change, and the adaptive capacity of fishes and fisheries to adjust to these changes. These questions are difficult to address, but long-term and large-scale datasets are becoming more readily available as a means to test hypotheses related to climate change. We hope this perspective will help researchers and decision makers identify research priorities and provide a framework to help sustain inland fish populations and fisheries for the diversity of users around the globe.
","language":"English","publisher":"Springer International","doi":"10.1007/s11160-017-9477-y","usgsCitation":"Paukert, C.P., Lynch, A.J., Beard, T., Chen, Y., Cooke, S., Cooperman, M.S., Cowx, I.G., Infante, D.M., Ibengwe, L., Myers, B., Nguyen, P.H., and Winfield, I.J., 2017, Designing a global assessment of climate change on inland fishes and fisheries: knowns and needs: Reviews in Fish Biology and Fisheries, v. 27, no. 2, p. 393-409, https://doi.org/10.1007/s11160-017-9477-y.","productDescription":"17 p.","startPage":"393","endPage":"409","ipdsId":"IP-085838","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469846,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s11160-017-9477-y","text":"External Repository"},{"id":348488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-15","publicationStatus":"PW","scienceBaseUri":"5a0425b8e4b0dc0b45b45375","contributors":{"authors":[{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":147821,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail J. 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":5645,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":false,"id":721334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, T. 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Assigning behaviors to accelerometer data often involves the use of captive animals or surrogate species, as their accelerometer signatures are generally assumed to be similar to those of their wild counterparts. However, this has rarely been tested. Validated accelerometer data are needed for polar bears Ursus maritimus to understand how habitat conditions may influence behavior and energy demands. We used accelerometer and water conductivity data to remotely distinguish 10 polar bear behaviors. We calibrated accelerometer and conductivity data collected from collars with behaviors observed from video-recorded captive polar bears and brown bears U. arctos, and with video from camera collars deployed on free-ranging polar bears on sea ice and on land. We used random forest models to predict behaviors and found strong ability to discriminate the most common wild polar bear behaviors using a combination of accelerometer and conductivity sensor data from captive or wild polar bears. In contrast, models using data from captive brown bears failed to reliably distinguish most active behaviors in wild polar bears. Our ability to discriminate behavior was greatest when species- and habitat-specific data from wild individuals were used to train models. Data from captive individuals may be suitable for calibrating accelerometers, but may provide reduced ability to discriminate some behaviors. The accelerometer calibrations developed here provide a method to quantify polar bear behaviors to evaluate the impacts of declines in Arctic sea ice.
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One challenging component for developing hydrologic budgets is quantifying water use through time because historical and site-specific water-use data can be sparse or poorly documented. This research developed a groundwater-use record for the Ozark Plateaus aquifer system (central USA) from 1900 to 2010 that related county-level aggregated water-use data to site-specific well locations and aquifer units. A simple population-based linear model, constrained to 0 million liters per day in 1900, provided the best means to extrapolate groundwater-withdrawal rates pre-1950s when there was a paucity of water-use data. To disaggregate county-level data to individual wells across a regional aquifer system, a programmatic hierarchical process was developed, based on the level of confidence that a well pumped groundwater for a specific use during a specific year. Statistical models tested on a subset of the best-available site-specific water-use data provided a mechanism to bracket historic groundwater use, such that groundwater-withdrawal rates ranged, on average, plus or minus 38% from modeled values. Groundwater withdrawn for public supply and domestic use accounted for between 48 and 74% of total groundwater use since 1901, highlighting that groundwater provides an important drinking-water resource. The compilation, analysis, and spatial and temporal extrapolation of water-use data remain a challenging task for water scientists, but is of paramount importance to better quantify groundwater use and availability.
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Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":695085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Worland, Scott C. 0000-0001-6384-2457 scworland@usgs.gov","orcid":"https://orcid.org/0000-0001-6384-2457","contributorId":5802,"corporation":false,"usgs":true,"family":"Worland","given":"Scott","email":"scworland@usgs.gov","middleInitial":"C.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":695086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westerman, Drew A. 0000-0002-8522-776X dawester@usgs.gov","orcid":"https://orcid.org/0000-0002-8522-776X","contributorId":4526,"corporation":false,"usgs":true,"family":"Westerman","given":"Drew","email":"dawester@usgs.gov","middleInitial":"A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":695087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Brian R. 0000-0001-6611-3807 brclark@usgs.gov","orcid":"https://orcid.org/0000-0001-6611-3807","contributorId":1502,"corporation":false,"usgs":true,"family":"Clark","given":"Brian","email":"brclark@usgs.gov","middleInitial":"R.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":695088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187707,"text":"70187707 - 2017 - Comparative cophylogenetics of Australian phabine pigeons and doves (Aves: Columbidae) and their feather lice (Insecta: Phthiraptera)","interactions":[],"lastModifiedDate":"2017-05-15T14:03:36","indexId":"70187707","displayToPublicDate":"2017-05-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2024,"text":"International Journal for Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Comparative cophylogenetics of Australian phabine pigeons and doves (Aves: Columbidae) and their feather lice (Insecta: Phthiraptera)","docAbstract":"Host–parasite coevolutionary histories can differ among multiple groups of parasites associated with the same group of hosts. For example, parasitic wing and body lice (Insecta: Phthiraptera) of New World pigeons and doves (Aves: Columbidae) differ in their cophylogenetic patterns, with body lice exhibiting higher phylogenetic congruence with their hosts than wing lice. In this study, we focus on the wing and body lice of Australian phabine pigeons and doves to determine whether the patterns in New World pigeons and doves are consistent with those of pigeons and doves from other regions. Using molecular sequence data for most phabine species and their lice, we estimated phylogenetic trees for all three groups (pigeons and doves, wing lice and body lice), and compared the phabine (host) tree with both parasite trees using multiple cophylogenetic methods. We found a pattern opposite to that found for New World pigeons and doves, with Australian wing lice showing congruence with their hosts, and body lice exhibiting a lack of congruence. There are no documented records of hippoboscid flies associated with Australian phabines, thus these lice may lack the opportunity to disperse among host species by attaching to hippoboscid flies (phoresis), which could explain these patterns. However, additional sampling for flies is needed to confirm this hypothesis. Large differences in body size among phabine pigeons and doves may also help to explain the congruence of the wing lice with their hosts. It may be more difficult for wing lice than body lice to switch among hosts that vary more dramatically in size. The results from this study highlight how host–parasite coevolutionary histories can vary by region, and how local factors can shape the relationship.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijpara.2016.12.003","usgsCitation":"Sweet, A.D., Chesser, R., and Johnson, K.P., 2017, Comparative cophylogenetics of Australian phabine pigeons and doves (Aves: Columbidae) and their feather lice (Insecta: Phthiraptera): International Journal for Parasitology, v. 47, no. 6, p. 347-356, https://doi.org/10.1016/j.ijpara.2016.12.003.","productDescription":"10 p.","startPage":"347","endPage":"356","ipdsId":"IP-081853","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":461595,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijpara.2016.12.003","text":"Publisher Index Page"},{"id":341310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe33e4b0a7fdb43c8be7","contributors":{"authors":[{"text":"Sweet, Andrew D.","contributorId":192032,"corporation":false,"usgs":false,"family":"Sweet","given":"Andrew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":695191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesser, R. Terry 0000-0003-4389-7092 tchesser@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":894,"corporation":false,"usgs":true,"family":"Chesser","given":"R. Terry","email":"tchesser@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":695190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Kevin P.","contributorId":192033,"corporation":false,"usgs":false,"family":"Johnson","given":"Kevin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":695192,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187124,"text":"ds1049 - 2017 - Coastal bathymetry data collected in May 2015 from Fire Island, New York—Wilderness breach and shoreface","interactions":[],"lastModifiedDate":"2017-07-24T13:40:45","indexId":"ds1049","displayToPublicDate":"2017-05-12T08:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1049","title":"Coastal bathymetry data collected in May 2015 from Fire Island, New York—Wilderness breach and shoreface","docAbstract":"Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida, conducted a bathymetric survey of Fire Island from May 6-20, 2015. The USGS is involved in a post-Hurricane Sandy effort to map and monitor the morphologic evolution of the wilderness breach as a part of the Hurricane Sandy Supplemental Project GS2-2B. During this study, bathymetry data were collected with single-beam echo sounders and Global Positioning Systems, which were mounted to personal watercraft, along the Fire Island shoreface and within the wilderness breach. Additional bathymetry and elevation data were collected using backpack Global Positioning Systems on flood shoals and in shallow channels within the wilderness breach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1049","usgsCitation":"Nelson, T.R., Miselis, J.L., Hapke, C.J., Brenner, O.T., Henderson, R.E., Reynolds, B.J., Wilson, K.E., 2017, Coastal bathymetry data collected in May 2015 from Fire Island, New York—Wilderness breach and shoreface: U.S. Geological Survey Data Series 1049, https://doi.org/10.3133/ds1049.\n\n","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073892","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":341095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1049/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"}},{"id":341094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1049/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.35,\n 40.55\n ],\n [\n -72.7,\n 40.55\n ],\n [\n -72.7,\n 40.866667\n ],\n [\n -73.35,\n 40.866667\n ],\n [\n -73.35,\n 40.55\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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This map product contains a set of three 1:18,000-scale maps showing the geology and structure of study areas in the western Candor Chasma region of Valles Marineris, Mars. These maps are part of an informal series of large-scale maps and map-based topical studies aimed at refining current understanding of the geologic history of western Candor Chasma. The map bases consist of digital elevation models and orthorectified images derived from High Resolution Imaging Science Experiment (HiRISE) data. These maps are accompanied by geologic cross sections, colorized elevation maps, and cutouts of HiRISE images showing key superposition relations. Also included in this product is a Correlation of Map Units that integrates units across all three map areas, as well as an integrated Description of Map Units and an integrated Explanation of Map Symbols. The maps were assembled using ArcGIS software produced by Environmental Systems Research Institute (http://www.esri.com). The ArcGIS projects and databases associated with each map are included online as supplemental data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3359","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Okubo, C.H., and Gaither, T.A., 2017, Bedrock and structural geologic maps of eastern Candor Sulci, western Ceti Mensa, and southeastern Ceti Mensa, Candor Chasma, Valles Marineris region of Mars: U.S. Geological Survey Scientific Investigations Map 3359, pamphlet 12 p., scale 1:18,000, https://doi.org/10.3133/sim3359.","productDescription":"2 Sheets: 46.89 x 39.22 inches and 43.00 x 51.70 inches; Pamphlet: i, 12 p.; GIS files; Basemaps; Metadata","onlineOnly":"N","ipdsId":"IP-073122","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":340816,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3359/sim3359_sheet.1.pdf","text":"Sheet 1","size":"42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3359"},{"id":340815,"rank":2,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sim/3359/sim3359_pamphlet.pdf","text":"Pamphlet","size":"944 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3359"},{"id":340817,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3359/sim3359_sheet2.pdf","text":"Sheet 2","size":"23.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3359"},{"id":341003,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3359/sim3359_gis.zip","text":"GIS Files","size":"502 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3359"},{"id":341074,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3359/sim3359_fullresolutionbasemaps.zip","text":"Full resolution basemaps","size":"3.7 GB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3359"},{"id":340818,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3359/sim3359_metadata.zip","text":"Metadata","size":"19.6 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3359"},{"id":340807,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3359/coverthb.jpg"}],"contact":"Astrogeology Research Program staff
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U.S. Geological Survey
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Flagstaff, AZ 86001
Pamphlet Contents
Because of a lack of clarity about the status of golden eagles (Aquila chrysaetos) in coastal southern California, the USGS, in collaboration with local, State, and other Federal agencies, began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in September 2014. During trapping efforts from September 29, 2014, to February 23, 2016, 27 golden eagles were captured. During trapping efforts from February 24, 2016, to February 23, 2017, an additional 10 golden eagles (7 females and 3 males) were captured in San Diego, Orange, and western Riverside Counties. Biotelemetry data for 26 of the 37 golden eagles that were transmitting data from February 24, 2016, to February 23, 2017 are presented. These eagles ranged as far north as northern Nevada and southern Wyoming, and as far south as La Paz, Baja California, Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1051","collaboration":"Prepared in cooperation with the San Diego Association of Governments (SANDAG) California Department of Fish and Wildlife, Bureau of Land Management and U.S. Fish and Wildlife Service","usgsCitation":"Tracey, J.A., Madden, M.C., Sebes, J.B., Bloom, P.H., Katzner, T.E., and Fisher, R.N., 2017, Biotelemetery data for golden eagles (Aquila chrysaetos) captured in coastal southern California, February 2016–February 2017: U.S. Geological Survey Data Series 1051, 35 p., https://doi.org/10.3133/ds1051.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-085815","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":341226,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1051/coverthb.jpg"},{"id":341227,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1051/ds1051.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1051"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 26\n ],\n [\n -106,\n 26\n ],\n [\n -106,\n 42\n ],\n [\n -122,\n 42\n ],\n [\n -122,\n 26\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
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Sacramento, California 95819
Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1801","usgsCitation":"Childress, E., and Letcher, B.H., 2017, Estimating thermal performance curves from repeated field observations: Ecology, v. 98, no. 5, p. 1377-1387, https://doi.org/10.1002/ecy.1801.","productDescription":"11 p.","startPage":"1377","endPage":"1387","ipdsId":"IP-082628","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":341242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-02","publicationStatus":"PW","scienceBaseUri":"5916c9afe4b044b359e48684","contributors":{"authors":[{"text":"Childress, Evan 0000-0001-8632-3078 echildress@usgs.gov","orcid":"https://orcid.org/0000-0001-8632-3078","contributorId":191994,"corporation":false,"usgs":true,"family":"Childress","given":"Evan","email":"echildress@usgs.gov","affiliations":[],"preferred":true,"id":694996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":149617,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","middleInitial":"H.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":694995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187564,"text":"70187564 - 2017 - Use of erroneous wolf generation time in assessments of domestic dog and human evolution","interactions":[],"lastModifiedDate":"2017-05-12T10:02:37","indexId":"70187564","displayToPublicDate":"2017-05-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Use of erroneous wolf generation time in assessments of domestic dog and human evolution","docAbstract":"Scientific interest in dog domestication and parallel evolution of dogs and humans (Wang et al. 2013) has increased recently (Freedman et al. 2014, Larson and Bradley 2014, Franz et al. 2016,), and various important conclusions have been drawn based on how long ago the calculations show dogs were domesticated from ancestral wolves (Canis lupus). Calculation of this duration is based on “the most commonly assumed mutation rate of 1 x 10-8 per generation and a 3-year gray wolf generation time . . .” (Skoglund et al. 2015:3). It is unclear on what information the assumed generation time is based, but Ersmark et al. (2016) seemed to have based their assumption on a single wolf (Mech and Seal 1987). The importance of assuring that such assumptions are valid is obvious. Recently, two independent studies employing three large data sets and three methods from two widely separated areas have found that wolf generation time is 4.2-4.7 years. The first study, based on 200 wolves in Yellowstone National Park used age-specific birth and death rates to calculate a generation time of 4.16 years (vonHoldt et al. 2008). The second, using estimated first-breeding times of 86 female wolves in northeastern Minnesota found a generation time of 4.3 years and using uterine examination of 159 female wolves from throughout Minnesota yielded a generation time of 4.7 years (Mech et al. 2016). We suggest that previous studies using a 3-year generation time recalculate their figures and adjust their conclusions based on these generation times and publish revised results.