Among shrubland- and young forest-nesting bird species in North America, Golden-winged Warblers (Vermivora chrysoptera) are one of the most rapidly declining partly because of limited nesting habitat. Creation and management of high quality vegetation communities used for nesting are needed to reduce declines. Thus, we examined whether common characteristics could be managed across much of the Golden-winged Warbler’s breeding range to increase daily survival rate (DSR) of nests. We monitored 388 nests on 62 sites throughout Minnesota, Wisconsin, New York, North Carolina, Pennsylvania, Tennessee, and West Virginia. We evaluated competing DSR models in spatial-temporal (dominant vegetation type, population segment, state, and year), intraseasonal (nest stage and time-within-season), and vegetation model suites. The best-supported DSR models among the three model suites suggested potential associations between daily survival rate of nests and state, time-within-season, percent grass and Rubus cover within 1 m of the nest, and distance to later successional forest edge. Overall, grass cover (negative association with DSR above 50%) and Rubus cover (DSR lowest at about 30%) within 1 m of the nest and distance to later successional forest edge (negative association with DSR) may represent common management targets across our states for increasing Golden-winged Warbler DSR, particularly in the Appalachian Mountains population segment. Context-specific adjustments to management strategies, such as in wetlands or areas of overlap with Blue-winged Warblers (Vermivora cyanoptera), may be necessary to increase DSR for Golden-winged Warblers.
","language":"English","publisher":" Society of Canadian Ornithologists; Bird Studies Canada","doi":"10.5751/ACE-00748-100106","usgsCitation":"Aldinger, K.R., Terhune, T.M., Wood, P.B., Buehler, D.A., Bakermans, M.H., Confer, ., Flaspohler, D.J., Larkin, J.L., Loegering, J.P., Percy, K.L., Roth, A.M., and Smalling, C.G., 2015, Variables associated with nest survival of Golden-winged Warblers (Vermivora chrysoptera) among vegetation communities commonly used for nesting: Avian Conservation and Ecology, v. 10, no. 1, Article 6; 12 p., https://doi.org/10.5751/ACE-00748-100106.","productDescription":"Article 6; 12 p.","ipdsId":"IP-052129","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":472433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-00748-100106","text":"Publisher Index Page"},{"id":340819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590c3dcbe4b0e541a038dd2b","contributors":{"authors":[{"text":"Aldinger, Kyle R.","contributorId":171892,"corporation":false,"usgs":false,"family":"Aldinger","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":693619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terhune, Theron M. II","contributorId":191720,"corporation":false,"usgs":false,"family":"Terhune","given":"Theron","suffix":"II","email":"","middleInitial":"M.","affiliations":[{"id":33355,"text":"Tall Timbers Research Station and Land Conservancy","active":true,"usgs":false}],"preferred":false,"id":694123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Petra Bohall pbwood@usgs.gov","contributorId":1791,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"Bohall","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":694124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":694125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bakermans, Marja H.","contributorId":169752,"corporation":false,"usgs":false,"family":"Bakermans","given":"Marja","email":"","middleInitial":"H.","affiliations":[{"id":33354,"text":"Worcester Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":694126,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Confer, John L.","contributorId":191748,"corporation":false,"usgs":false,"family":"Confer","given":" John L.","affiliations":[{"id":18877,"text":"Ithaca College","active":true,"usgs":false}],"preferred":false,"id":694127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flaspohler, David J.","contributorId":191721,"corporation":false,"usgs":false,"family":"Flaspohler","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":18877,"text":"Ithaca College","active":true,"usgs":false},{"id":16650,"text":"School of Forest Resources & Environmental Science, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":694128,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Larkin, Jeffrey L.","contributorId":169747,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":34542,"text":"Department of Biology. Indiana University of Pennsylvania","active":true,"usgs":false},{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":694129,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loegering, John P.","contributorId":166933,"corporation":false,"usgs":false,"family":"Loegering","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":33353,"text":"University of Minnesota, Crookston","active":true,"usgs":false}],"preferred":false,"id":694130,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Percy, Katie L.","contributorId":191722,"corporation":false,"usgs":false,"family":"Percy","given":"Katie","email":"","middleInitial":"L.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":694131,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Roth, Amber M.","contributorId":191723,"corporation":false,"usgs":false,"family":"Roth","given":"Amber","email":"","middleInitial":"M.","affiliations":[{"id":25614,"text":"School of Forest Resources, University of Maine","active":true,"usgs":false},{"id":27866,"text":"University of Maine, Department of Wildlife, Fisheries, and Conservation Biology, Orono, ME","active":true,"usgs":false},{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":694132,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smalling, Curtis G.","contributorId":191724,"corporation":false,"usgs":false,"family":"Smalling","given":"Curtis","email":"","middleInitial":"G.","affiliations":[{"id":33352,"text":"Audubon North Carolina","active":true,"usgs":false}],"preferred":false,"id":694133,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70187265,"text":"70187265 - 2015 - Radio-transmitters have no impact on survival of pre-fledged American Woodcocks","interactions":[],"lastModifiedDate":"2017-04-27T10:35:47","indexId":"70187265","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Radio-transmitters have no impact on survival of pre-fledged American Woodcocks","docAbstract":"American Woodcocks (Scolopax minor) are a high priority species of conservation need across most of their breeding range due to long-term population declines. Survival of juveniles may be key to understanding these population declines, but there have been few direct estimates of juvenile woodcock survival rates, and no recent assessment of the possible effect of radio-tagging on juvenile survival. In 2011 and 2012, we radio-tagged 73 juvenile American Woodcocks in west-central Minnesota and compared survival rates of radio-tagged (N = 58) and non-radio-tagged (N = 82) juveniles during the period from hatching to fledging. We compared survival rates of juveniles with known fates and used logistic-exposure models to assess the potential impact of radio-transmitters on survival. We evaluated variables related to juvenile survival including age, hatch date, maximum and minimum temperature, precipitation, and year to assess the possible effects of radio-transmitters. The best-supported model of survival rate of juvenile American Woodcocks included the interaction of age and year and a negative effect of precipitation (β = −0.76, 85% CI: −1.08 to −0.43), but did not include a negative effect of transmitters. Our results suggest that radio-transmitters did not impact survival of juvenile American Woodcocks and that transmitters are a reliable tool for studying survival of juvenile American Woodcocks, and perhaps other precocial shorebirds.
","language":"English","publisher":"Wiley","doi":"10.1656/058.012.0107","usgsCitation":"Daly, K.O., Andersen, D., Brininger, W.L., and Cooper, T.R., 2015, Radio-transmitters have no impact on survival of pre-fledged American Woodcocks: Journal of Field Ornithology, v. 86, no. 4, p. 345-351, https://doi.org/10.1656/058.012.0107.","productDescription":"7 p.","startPage":"345","endPage":"351","ipdsId":"IP-066725","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"86","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030328e4b0e862d230f74d","contributors":{"authors":[{"text":"Daly, Kyle O.","contributorId":191466,"corporation":false,"usgs":false,"family":"Daly","given":"Kyle","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":693130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":2168,"corporation":false,"usgs":true,"family":"Andersen","given":"David E.","email":"dea@usgs.gov","affiliations":[{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brininger, Wayne L.","contributorId":191467,"corporation":false,"usgs":false,"family":"Brininger","given":"Wayne","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cooper, Thomas R.","contributorId":191468,"corporation":false,"usgs":false,"family":"Cooper","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":693132,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191871,"text":"70191871 - 2015 - Climate change in the Northeast and Midwest United States","interactions":[],"lastModifiedDate":"2020-07-29T14:04:14.1715","indexId":"70191871","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"chapter":"1","title":"Climate change in the Northeast and Midwest United States","docAbstract":"The climate is changing rapidly in ways that have already impacted wildlife and their habitats. Here, we present a summary of the observed past and projected future climate changes in the region that are relevant to wildlife and ecosystems, as well as what we know and don’t know in order to raise managers’ confidence in their planning. A number of large-scale regional changes affect the overall terrestrial landscape within the Northeast and Midwest United States:
In addition, localized climate change is occurring in specific regions:
In the short term (i.e., over the next 5-20 years), the direction and magnitude of warming in the global climate are mostly consistent across all emissions scenarios and with strong agreement across models. Accordingly, we are certain that the Northeast and Midwest will see longer growing seasons. We are likely to see shifts from snow to rain, though shifts in the amount of total precipitation (rain and snow) are less certain. Severe weather events (e.g., thunderstorms, tornadoes) are challenging to detect. Soil moisture and evapotranspiration trends are neither robustly observed nor consistent amongst modeling studies.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Integrating climate change into northeast and midwest State Wildlife Action Plans","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Northeast Climate Science Center","usgsCitation":"Bryan, A., Karmalkar, A., Coffel, E., Ning, L., Horton, R.M., Demaria, E., Fan, F., Bradley, R.S., and Palmer, R., 2015, Climate change in the Northeast and Midwest United States, 51 p.","productDescription":"51 p.","startPage":"6","endPage":"57","ipdsId":"IP-065184","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":352203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346866,"type":{"id":15,"text":"Index Page"},"url":"https://necsc.umass.edu/projects/integrating-climate-change-state-wildlife-action-plans"}],"country":"United States","state":"Connecticut, Delaware, Iowa, Illinois, 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\"}}]}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afeebefe4b0da30c1bfc6a4","contributors":{"authors":[{"text":"Bryan, Alexander 0000-0003-2040-7636 abryan@usgs.gov","orcid":"https://orcid.org/0000-0003-2040-7636","contributorId":168822,"corporation":false,"usgs":true,"family":"Bryan","given":"Alexander","email":"abryan@usgs.gov","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":713473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karmalkar, Ambarish","contributorId":202897,"corporation":false,"usgs":false,"family":"Karmalkar","given":"Ambarish","affiliations":[],"preferred":false,"id":730149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coffel, Ethan","contributorId":202898,"corporation":false,"usgs":false,"family":"Coffel","given":"Ethan","email":"","affiliations":[],"preferred":false,"id":730150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ning, Liang","contributorId":202899,"corporation":false,"usgs":false,"family":"Ning","given":"Liang","email":"","affiliations":[],"preferred":false,"id":730151,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horton, Radley M.","contributorId":139267,"corporation":false,"usgs":false,"family":"Horton","given":"Radley","email":"","middleInitial":"M.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":730152,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Demaria, Eleonora","contributorId":202900,"corporation":false,"usgs":false,"family":"Demaria","given":"Eleonora","email":"","affiliations":[],"preferred":false,"id":730153,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fan, Fanxing","contributorId":202901,"corporation":false,"usgs":false,"family":"Fan","given":"Fanxing","email":"","affiliations":[],"preferred":false,"id":730154,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bradley, Raymond S.","contributorId":202902,"corporation":false,"usgs":false,"family":"Bradley","given":"Raymond","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":730155,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Palmer, Richard","contributorId":202903,"corporation":false,"usgs":false,"family":"Palmer","given":"Richard","affiliations":[],"preferred":false,"id":730156,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70191822,"text":"70191822 - 2015 - Plague bacterium as a transformer species in prairie dogs and the grasslands of western North America","interactions":[],"lastModifiedDate":"2017-10-18T10:34:40","indexId":"70191822","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Plague bacterium as a transformer species in prairie dogs and the grasslands of western North America","docAbstract":"Invasive transformer species change the character, condition, form, or nature of ecosystems and deserve considerable attention from conservation scientists. We applied the transformer species concept to the plague bacterium Yersinia pestis in western North America, where the pathogen was introduced around 1900. Y. pestis transforms grassland ecosystems by severely depleting the abundance of prairie dogs (Cynomys spp.) and thereby causing declines in native species abundance and diversity, including threatened and endangered species; altering food web connections; altering the import and export of nutrients; causing a loss of ecosystem resilience to encroaching invasive plants; and modifying prairie dog burrows. Y. pestis poses an important challenge to conservation biologists because it causes trophic-level perturbations that affect the stability of ecosystems. Unfortunately, understanding of the effects of Y. pestis on ecosystems is rudimentary, highlighting an acute need for continued research.
","language":"English","publisher":"Wiley","doi":"10.1111/cobi.12498","usgsCitation":"Eads, D.A., and Biggins, D.E., 2015, Plague bacterium as a transformer species in prairie dogs and the grasslands of western North America: Conservation Biology, v. 29, no. 4, p. 1086-1093, https://doi.org/10.1111/cobi.12498.","productDescription":"8 p.","startPage":"1086","endPage":"1093","ipdsId":"IP-064632","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-28","publicationStatus":"PW","scienceBaseUri":"59e8683de4b05fe04cd4d249","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":713233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713232,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188061,"text":"70188061 - 2015 - Projecting the spatiotemporal carbon dynamics of the Greater Yellowstone Ecosystem from 2006 to 2050","interactions":[],"lastModifiedDate":"2017-05-30T13:20:45","indexId":"70188061","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1183,"text":"Carbon Balance and Management","active":true,"publicationSubtype":{"id":10}},"title":"Projecting the spatiotemporal carbon dynamics of the Greater Yellowstone Ecosystem from 2006 to 2050","docAbstract":"Background
Climate change and the concurrent change in wildfire events and land use comprehensively affect carbon dynamics in both spatial and temporal dimensions. The purpose of this study was to project the spatial and temporal aspects of carbon storage in the Greater Yellowstone Ecosystem (GYE) under these changes from 2006 to 2050. We selected three emission scenarios and produced simulations with the CENTURY model using three General Circulation Models (GCMs) for each scenario. We also incorporated projected land use change and fire occurrence into the carbon accounting.
Results
The three GCMs showed increases in maximum and minimum temperature, but precipitation projections varied among GCMs. Total ecosystem carbon increased steadily from 7,942 gC/m2 in 2006 to 10,234 gC/m2 in 2050 with an annual rate increase of 53 gC/m2/year. About 56.6% and 27% of the increasing rate was attributed to total live carbon and total soil carbon, respectively. Net Primary Production (NPP) increased slightly from 260 gC/m2/year in 2006 to 310 gC/m2/year in 2050 with an annual rate increase of 1.22 gC/m2/year. Forest clear-cutting and fires resulted in direct carbon removal; however, the rate was low at 2.44 gC/m2/year during 2006–2050. The area of clear-cutting and wildfires in the GYE would account for 10.87% of total forested area during 2006–2050, but the predictive simulations demonstrated different spatial distributions in national forests and national parks.
Conclusions
The GYE is a carbon sink during 2006–2050. The capability of vegetation is almost double that of soil in terms of sequestering extra carbon. Clear-cutting and wildfires in GYE will affect 10.87% of total forested area, but direct carbon removal from clear-cutting and fires is 109.6 gC/m2, which accounts for only 1.2% of the mean ecosystem carbon level of 9,056 gC/m2, and thus is not significant.
State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine (Pinus albicaulis). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.
","language":"English","publisher":"AIM Press","doi":"10.3934/environsci.2015.2.400","usgsCitation":"Miller, B.W., Frid, L., Chang, T., Piekielek, N.B., Hansen, A.J., and Morisette, J.T., 2015, Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change: AIMS Environmental Science, v. 2, no. 2, p. 400-426, https://doi.org/10.3934/environsci.2015.2.400.","productDescription":"27 p.","startPage":"400","endPage":"426","ipdsId":"IP-065083","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":472396,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/environsci.2015.2.400","text":"Publisher Index Page"},{"id":345943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c37e3be4b091459a631703","contributors":{"authors":[{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":710899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frid, Leonardo","contributorId":196604,"corporation":false,"usgs":false,"family":"Frid","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":710900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chang, Tony","contributorId":191992,"corporation":false,"usgs":false,"family":"Chang","given":"Tony","email":"","affiliations":[],"preferred":false,"id":710901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Piekielek, N. B.","contributorId":127648,"corporation":false,"usgs":false,"family":"Piekielek","given":"N.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":710902,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Andrew J.","contributorId":196605,"corporation":false,"usgs":false,"family":"Hansen","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":710903,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":710898,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189176,"text":"70189176 - 2015 - High‐resolution trench photomosaics from image‐based modeling: Workflow and error analysis","interactions":[],"lastModifiedDate":"2020-12-18T21:22:00.978516","indexId":"70189176","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"High‐resolution trench photomosaics from image‐based modeling: Workflow and error analysis","docAbstract":"Photomosaics are commonly used to construct maps of paleoseismic trench exposures, but the conventional process of manually using image‐editing software is time consuming and produces undesirable artifacts and distortions. Herein, we document and evaluate the application of image‐based modeling (IBM) for creating photomosaics and 3D models of paleoseismic trench exposures, illustrated with a case‐study trench across the Wasatch fault in Alpine, Utah. Our results include a structure‐from‐motion workflow for the semiautomated creation of seamless, high‐resolution photomosaics designed for rapid implementation in a field setting. Compared with conventional manual methods, the IBM photomosaic method provides a more accurate, continuous, and detailed record of paleoseismic trench exposures in approximately half the processing time and 15%–20% of the user input time. Our error analysis quantifies the effect of the number and spatial distribution of control points on model accuracy. For this case study, an ∼87 m2 exposure of a benched trench photographed at viewing distances of 1.5–7 m yields a model with <2 cm root mean square error (rmse) with as few as six control points. Rmse decreases as more control points are implemented, but the gains in accuracy are minimal beyond 12 control points. Spreading control points throughout the target area helps to minimize error. We propose that 3D digital models and corresponding photomosaics should be standard practice in paleoseismic exposure archiving. The error analysis serves as a guide for future investigations that seek balance between speed and accuracy during photomosaic and 3D model construction.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120150041","usgsCitation":"Reitman, N.G., Bennett, S.E., Gold, R.D., Briggs, R.W., and DuRoss, C., 2015, High‐resolution trench photomosaics from image‐based modeling: Workflow and error analysis: Bulletin of the Seismological Society of America, v. 105, no. 5, p. 2354-2366, https://doi.org/10.1785/0120150041.","productDescription":"13 p.","startPage":"2354","endPage":"2366","ipdsId":"IP-065030","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":352939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-01","publicationStatus":"PW","scienceBaseUri":"5afeebefe4b0da30c1bfc6aa","contributors":{"authors":[{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":703366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bennett, Scott E.K. 0000-0002-9772-4122 sekbennett@usgs.gov","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":5340,"corporation":false,"usgs":true,"family":"Bennett","given":"Scott","email":"sekbennett@usgs.gov","middleInitial":"E.K.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":703367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":703368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":703369,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":703370,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188386,"text":"70188386 - 2015 - Kinematics of shallow backthrusts in the Seattle fault zone, Washington State","interactions":[],"lastModifiedDate":"2017-06-07T14:28:11","indexId":"70188386","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Kinematics of shallow backthrusts in the Seattle fault zone, Washington State","docAbstract":"Near-surface thrust fault splays and antithetic backthrusts at the tips of major thrust fault systems can distribute slip across multiple shallow fault strands, complicating earthquake hazard analyses based on studies of surface faulting. The shallow expression of the fault strands forming the Seattle fault zone of Washington State shows the structural relationships and interactions between such fault strands. Paleoseismic studies document an ∼7000 yr history of earthquakes on multiple faults within the Seattle fault zone, with some backthrusts inferred to rupture in small (M ∼5.5–6.0) earthquakes at times other than during earthquakes on the main thrust faults. We interpret seismic-reflection profiles to show three main thrust faults, one of which is a blind thrust fault directly beneath downtown Seattle, and four small backthrusts within the Seattle fault zone. We then model fault slip, constrained by shallow deformation, to show that the Seattle fault forms a fault propagation fold rather than the alternatively proposed roof thrust system. Fault slip modeling shows that back-thrust ruptures driven by moderate (M ∼6.5–6.7) earthquakes on the main thrust faults are consistent with the paleoseismic data. The results indicate that paleoseismic data from the back-thrust ruptures reveal the times of moderate earthquakes on the main fault system, rather than indicating smaller (M ∼5.5–6.0) earthquakes involving only the backthrusts. Estimates of cumulative shortening during known Seattle fault zone earthquakes support the inference that the Seattle fault has been the major seismic hazard in the northern Cascadia forearc in the late Holocene.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01179.1","usgsCitation":"Pratt, T.L., Troost, K., Odum, J., and Stephenson, W.J., 2015, Kinematics of shallow backthrusts in the Seattle fault zone, Washington State: Geosphere, v. 11, no. 6, p. 1948-1974, https://doi.org/10.1130/GES01179.1.","productDescription":"27 p. ","startPage":"1948","endPage":"1974","ipdsId":"IP-066951","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":472440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01179.1","text":"Publisher Index Page"},{"id":342255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Seattle ","otherGeospatial":"Seattle Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.365234375,\n 46.604167162931844\n ],\n [\n -121.61865234375,\n 46.604167162931844\n ],\n [\n -121.61865234375,\n 48.23930899024907\n ],\n [\n -124.365234375,\n 48.23930899024907\n ],\n [\n -124.365234375,\n 46.604167162931844\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-12","publicationStatus":"PW","scienceBaseUri":"593910b3e4b0764e6c5e88bb","contributors":{"authors":[{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Troost, K.G.","contributorId":192716,"corporation":false,"usgs":false,"family":"Troost","given":"K.G.","email":"","affiliations":[],"preferred":false,"id":697489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odum, Jackson K. 0000-0003-4697-2430 odum@usgs.gov","orcid":"https://orcid.org/0000-0003-4697-2430","contributorId":1365,"corporation":false,"usgs":true,"family":"Odum","given":"Jackson K.","email":"odum@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188389,"text":"70188389 - 2015 - Thin‐ or thick‐skinned faulting in the Yakima fold and thrust belt (WA)? Constraints from kinematic modeling of the saddle mountains anticline","interactions":[],"lastModifiedDate":"2017-06-07T15:03:37","indexId":"70188389","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Thin‐ or thick‐skinned faulting in the Yakima fold and thrust belt (WA)? Constraints from kinematic modeling of the saddle mountains anticline","docAbstract":"The Yakima fold and thrust belt (YFTB) deforms the Columbia River Basalt Group flows of Washington State. The YFTB fault geometries and slip rates are crucial parameters for seismic‐hazard assessments of nearby dams and nuclear facilities, yet there are competing models for the subsurface fault geometry involving shallowly rooted versus deeply rooted fault systems. The YFTB is also thought to be analogous to the evenly spaced wrinkle ridges found on other terrestrial planets. Using seismic reflection data, borehole logs, and surface geologic data, we tested two proposed kinematic end‐member thick‐ and thin‐skinned fault models beneath the Saddle Mountains anticline of the YFTB. Observed subsurface geometry can be produced by 600–800 m of heave along a single listric‐reverse fault or ∼3.5 km of slip along two superposed low‐angle thrust faults. Both models require decollement slip between 7 and 9 km depth, resulting in greater fault areas than sometimes assumed in hazard assessments. Both models require initial slip much earlier than previously thought and may provide insight into the subsurface geometry of analogous comparisons to wrinkle ridges observed on other planets.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120140207","usgsCitation":"Casale, G., and Pratt, T.L., 2015, Thin‐ or thick‐skinned faulting in the Yakima fold and thrust belt (WA)? Constraints from kinematic modeling of the saddle mountains anticline: Bulletin of the Seismological Society of America, v. 105, no. 2A, p. 745-752, https://doi.org/10.1785/0120140207.","productDescription":"7 p.","startPage":"745","endPage":"752","ipdsId":"IP-051433","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima fold and thrust belt","volume":"105","issue":"2A","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-01-27","publicationStatus":"PW","scienceBaseUri":"593910b2e4b0764e6c5e88af","contributors":{"authors":[{"text":"Casale, Gabriele 0000-0003-1371-753X","orcid":"https://orcid.org/0000-0003-1371-753X","contributorId":192726,"corporation":false,"usgs":false,"family":"Casale","given":"Gabriele","email":"","affiliations":[{"id":27675,"text":"Appalachian State University, Boone, NC","active":true,"usgs":false}],"preferred":false,"id":697513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697512,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187276,"text":"70187276 - 2015 - Influence of Smallmouth Bass predation on recruitment of age-0 Yellow Perch in South Dakota glacial lakes","interactions":[],"lastModifiedDate":"2017-04-28T10:53:24","indexId":"70187276","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Influence of Smallmouth Bass predation on recruitment of age-0 Yellow Perch in South Dakota glacial lakes","docAbstract":"We estimated the influence of predation by Smallmouth Bass Micropterus dolomieu on recruitment of age-0 Yellow Perch Perca flavescens in two northeastern South Dakota glacial lakes. We estimated a likely range in consumption of age-0 Yellow Perch using Smallmouth Bass diet information from two time periods when age-0 Yellow Perch constituted high (2008) and low (2012 and 2013) proportions of Smallmouth Bass diets, and bass population size estimates as inputs in a bioenergetics model. The proportion of age-0 Yellow Perch consumed by the Smallmouth Bass populations was determined by comparing estimates of consumption with estimates of age-0 perch production. During 2008, age-0 Yellow Perch constituted between 0% and 42% of Smallmouth Bass diets by weight, whereas during 2012 and 2013, age-0 perch constituted between 0% and 20% of bass diets by weight. Across both lakes and time periods, production of age-0 Yellow Perch ranged from 0.32 to 1.78 kg·ha−1·week−1. Estimates of Smallmouth Bass consumption measured during the same intervals ranged from 0.06 to 0.33 kg·ha−1·week−1, equating to consumption of between 1% and 34% of the available Yellow Perch biomass. Given current conditions relative to Smallmouth Bass abundance and consumption dynamics and production of age-0 Yellow Perch, it does not appear that Smallmouth Bass predation acts as a singular factor limiting recruitment of age-0 Yellow Perch in our study lakes. However, future research and management initiatives should recognize that the long-term impact of Smallmouth Bass predation is not static and will likely fluctuate depending on environmental (e.g., temperature) and biotic (e.g., trends in macrophyte abundance, predator and prey population structure and abundance, and predatory fish assemblage dynamics) characteristics.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2015.1044629","usgsCitation":"Dembkowski, D., Willis, D., Blackwell, B.G., Chipps, S.R., Bacula, T.D., and Wuellner, M., 2015, Influence of Smallmouth Bass predation on recruitment of age-0 Yellow Perch in South Dakota glacial lakes: North American Journal of Fisheries Management, v. 35, no. 4, p. 736-747, https://doi.org/10.1080/02755947.2015.1044629.","productDescription":"12 p.","startPage":"736","endPage":"747","ipdsId":"IP-064727","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-13","publicationStatus":"PW","scienceBaseUri":"590454a8e4b022cee40dc258","contributors":{"authors":[{"text":"Dembkowski, Daniel J.","contributorId":78237,"corporation":false,"usgs":true,"family":"Dembkowski","given":"Daniel J.","affiliations":[],"preferred":false,"id":693451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willis, D.W.","contributorId":56179,"corporation":false,"usgs":true,"family":"Willis","given":"D.W.","email":"","affiliations":[],"preferred":false,"id":693452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blackwell, B. G.","contributorId":191556,"corporation":false,"usgs":false,"family":"Blackwell","given":"B.","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":693453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693184,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bacula, T. D.","contributorId":191557,"corporation":false,"usgs":false,"family":"Bacula","given":"T.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":693454,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wuellner, M.R.","contributorId":60867,"corporation":false,"usgs":true,"family":"Wuellner","given":"M.R.","affiliations":[],"preferred":false,"id":693455,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187285,"text":"70187285 - 2015 - Day-roost tree selection by northern long-eared bats—What do non-roost tree comparisons and one year of data really tell us?","interactions":[],"lastModifiedDate":"2017-04-27T17:06:01","indexId":"70187285","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Day-roost tree selection by northern long-eared bats—What do non-roost tree comparisons and one year of data really tell us?","docAbstract":"Bat day-roost selection often is described through comparisons of day-roosts with randomly selected, and assumed unused, trees. Relatively few studies, however, look at patterns of multi-year selection or compare day-roosts used across years. We explored day-roost selection using 2 years of roost selection data for female northern long-eared bats (Myotis septentrionalis) on the Fort Knox Military Reservation, Kentucky, USA. We compared characteristics of randomly selected non-roost trees and day-roosts using a multinomial logistic model and day-roost species selection using chi-squared tests. We found that factors differentiating day-roosts from non-roosts and day-roosts between years varied. Day-roosts differed from non-roosts in the first year of data in all measured factors, but only in size and decay stage in the second year. Between years, day-roosts differed in size and canopy position, but not decay stage. Day-roost species selection was non-random and did not differ between years. Although bats used multiple trees, our results suggest that there were additional unused trees that were suitable as roosts at any time. Day-roost selection pattern descriptions will be inadequate if based only on a single year of data, and inferences of roost selection based only on comparisons of roost to non-roosts should be limited.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2015.03.008","usgsCitation":"Silvis, A., Ford, W.M., and Britzke, E.R., 2015, Day-roost tree selection by northern long-eared bats—What do non-roost tree comparisons and one year of data really tell us?: Global Ecology and Conservation, v. 3, p. 756-763, https://doi.org/10.1016/j.gecco.2015.03.008.","productDescription":"8 p.","startPage":"756","endPage":"763","ipdsId":"IP-062821","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":472422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2015.03.008","text":"Publisher Index Page"},{"id":340550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030328e4b0e862d230f74b","contributors":{"authors":[{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":693309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark wford@usgs.gov","contributorId":3858,"corporation":false,"usgs":true,"family":"Ford","given":"W.","email":"wford@usgs.gov","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":693216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Britzke, Eric R.","contributorId":8327,"corporation":false,"usgs":true,"family":"Britzke","given":"Eric","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":693310,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188440,"text":"70188440 - 2015 - Thermokarst lake methanogenesis along a complete talik profile","interactions":[],"lastModifiedDate":"2017-06-09T14:07:53","indexId":"70188440","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Thermokarst lake methanogenesis along a complete talik profile","docAbstract":"Thermokarst (thaw) lakes emit methane (CH4) to the atmosphere formed from thawed permafrost organic matter (OM), but the relative magnitude of CH4 production in surface lake sediments vs. deeper thawed permafrost horizons is not well understood. We assessed anaerobic CH4 production potentials from various depths along a 590 cm long lake sediment core that captured the entire sediment package of the talik (thaw bulb) beneath the center of an interior Alaska thermokarst lake, Vault Lake, and the top 40 cm of thawing permafrost beneath the talik. We also studied the adjacent Vault Creek permafrost tunnel that extends through ice-rich yedoma permafrost soils surrounding the lake and into underlying gravel. Our results showed CH4 production potentials were highest in the organic-rich surface lake sediments, which were 151 cm thick (mean ± SD: 5.95 ± 1.67 μg C–CH4 g dw−1 d−1; 125.9 ± 36.2 μg C–CH4 g C−1org d−1). High CH4 production potentials were also observed in recently thawed permafrost (1.18 ± 0.61 μg C–CH4g dw−1 d−1; 59.60± 51.5 μg C–CH4 g C−1org d−1) at the bottom of the talik, but the narrow thicknesses (43 cm) of this horizon limited its overall contribution to total sediment column CH4 production in the core. Lower rates of CH4 production were observed in sediment horizons representing permafrost that has been thawing in the talik for a longer period of time. No CH4 production was observed in samples obtained from the permafrost tunnel, a non-lake environment. Our findings imply that CH4production is highly variable in thermokarst lake systems and that both modern OM supplied to surface sediments and ancient OM supplied to both surface and deep lake sediments by in situ thaw and shore erosion of yedoma permafrost are important to lake CH4 production.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-12-4317-2015","usgsCitation":"Heslop, J., Walter Anthony, K., Sepulveda-Jauregui, A., Martinez-Cruz, K., Bondurant, A., Grosse, G., and Jones, M.C., 2015, Thermokarst lake methanogenesis along a complete talik profile: Biogeosciences, v. 12, p. 4317-4331, https://doi.org/10.5194/bg-12-4317-2015.","productDescription":"15 p.","startPage":"4317","endPage":"4331","ipdsId":"IP-064594","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":488681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-12-4317-2015","text":"Publisher Index Page"},{"id":342339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"593bb3a9e4b0764e6c60e7eb","contributors":{"authors":[{"text":"Heslop, J.K.","contributorId":192801,"corporation":false,"usgs":false,"family":"Heslop","given":"J.K.","email":"","affiliations":[],"preferred":false,"id":697757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter Anthony, K.M.","contributorId":169384,"corporation":false,"usgs":false,"family":"Walter Anthony","given":"K.M.","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":697758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda-Jauregui, A.","contributorId":192802,"corporation":false,"usgs":false,"family":"Sepulveda-Jauregui","given":"A.","email":"","affiliations":[],"preferred":false,"id":697759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martinez-Cruz, K.","contributorId":192803,"corporation":false,"usgs":false,"family":"Martinez-Cruz","given":"K.","email":"","affiliations":[],"preferred":false,"id":697760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bondurant, A.","contributorId":192804,"corporation":false,"usgs":false,"family":"Bondurant","given":"A.","affiliations":[],"preferred":false,"id":697761,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grosse, G.","contributorId":192805,"corporation":false,"usgs":false,"family":"Grosse","given":"G.","email":"","affiliations":[],"preferred":false,"id":697762,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":697756,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70191852,"text":"70191852 - 2015 - Estimating bighorn sheep (Ovis canadensis) abundance using noninvasive sampling at a mineral lick within a National Park Wilderness Area","interactions":[],"lastModifiedDate":"2017-10-18T14:11:06","indexId":"70191852","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Estimating bighorn sheep (Ovis canadensis) abundance using noninvasive sampling at a mineral lick within a National Park Wilderness Area","title":"Estimating bighorn sheep (Ovis canadensis) abundance using noninvasive sampling at a mineral lick within a National Park Wilderness Area","docAbstract":"Conservation of species requires accurate population estimates. We used genetic markers from feces to determine bighorn sheep abundance for a herd that was hypothesized to be declining and in need of population status monitoring. We sampled from a small but accessible portion of the population's range where animals naturally congregate at a natural mineral lick to test whether we could accurately estimate population size by sampling from an area where animals concentrate. We used mark-recapture analysis to derive population estimates, and compared estimates from this smaller spatial sampling to estimates from sampling of the entire bighorn sheep range. We found that estimates were somewhat comparable; in 2009, the mineral lick sample and entire range sample differed by 20 individuals, and in 2010 they differed by only one individual. However, we captured 13 individuals in the entire range sample that were not captured at the mineral lick, and thus violated a model assumption that all individuals had an equal opportunity of being captured. This eliminated the possibility of inferring a total population estimate from just animals visiting the mineral lick, but because estimates were relatively similar, monitoring at the mineral lick can provide a useful index for management and conservation. We compared our results to a radio-collar study conducted in 2003–2004 and confirmed that the population remained stable since 2004. Our population estimates were 78 (CI 62–114) in 2009 and 95 (CI 77–131) in 2010. Between 7 and 11 sampling dates were needed to achieve a CV of 20% for population estimates, assuming a capture probability between 0.09 and 0.13. We relied on citizen science volunteers to maximize data collection and reduce costs; 71% of all fecal samples were collected by volunteers, compared to 29% collected by paid staff. We conclude that our technique provides a useful monitoring tool for managers. The technique could be tested and applied in similar populations where animals congregate with high fidelity at a mineral lick or other area.
","language":"English","publisher":"Monte L. Bean Life Science Museum, Brigham Young University","doi":"10.3398/064.075.0206","usgsCitation":"Schoenecker, K.A., Watry, M.K., Ellison, L.E., Schwarz, M.A., and Luikart, G., 2015, Estimating bighorn sheep (Ovis canadensis) abundance using noninvasive sampling at a mineral lick within a National Park Wilderness Area: Western North American Naturalist, v. 75, no. 2, p. 181-191, https://doi.org/10.3398/064.075.0206.","productDescription":"11 p.","startPage":"181","endPage":"191","ipdsId":"IP-053380","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":502522,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsarchive.byu.edu/wnan/vol75/iss2/5","text":"External Repository"},{"id":346872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.71662902832031,\n 40.40774498177989\n ],\n [\n -105.45433044433594,\n 40.40774498177989\n ],\n [\n -105.45433044433594,\n 40.51171103483292\n ],\n [\n -105.71662902832031,\n 40.51171103483292\n ],\n [\n -105.71662902832031,\n 40.40774498177989\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e8683de4b05fe04cd4d245","contributors":{"authors":[{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X schoeneckerk@usgs.gov","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":2001,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn","email":"schoeneckerk@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watry, Mary Kay","contributorId":141021,"corporation":false,"usgs":false,"family":"Watry","given":"Mary","email":"","middleInitial":"Kay","affiliations":[{"id":7237,"text":"NPS, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":713399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellison, Laura E. ellisonl@usgs.gov","contributorId":3220,"corporation":false,"usgs":true,"family":"Ellison","given":"Laura","email":"ellisonl@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwarz, Michael A.","contributorId":197399,"corporation":false,"usgs":false,"family":"Schwarz","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":713401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Luikart, Gordon","contributorId":124531,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":5091,"text":"Flathead Lake Biological Station, Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Polson, MT 59860, USA","active":true,"usgs":false}],"preferred":false,"id":713402,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191883,"text":"70191883 - 2015 - Phenological response of an Arizona dryland forest to short-term climatic extremes","interactions":[],"lastModifiedDate":"2017-10-18T16:36:22","indexId":"70191883","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Phenological response of an Arizona dryland forest to short-term climatic extremes","docAbstract":"Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of site characteristics and climatic conditions on the phenological patterns of an Arizona, USA, ponderosa pine (Pinus ponderosa) forest during a five-year period (2005 to 2009) that encompassed extreme wet and dry precipitation regimes. We assembled 80 synthetic Landsat images by applying the spatial and temporal adaptive reflectance fusion method (STARFM) to 500 m MODIS and 30 m Landsat-5 Thematic Mapper (TM) data. We tested relationships between site characteristics and the timing of peak Normalized Difference Vegetation Index (NDVI) to assess the effect of climatic stress on the green-up of individual pixels during or after the summer monsoon. Our results show that drought-induced stress led to a fragmented phenological response that was highly dependent on microsite parameters, as both the spatial autocorrelation of peak timing and the number of significant site variables increased during the drought year. Pixels at lower elevations and with higher proportions of herbaceous vegetation were more likely to exhibit dynamic responses to changes in precipitation conditions. Our study demonstrates the complexity of responses within dryland forest ecosystems and highlights the need for standardized monitoring of phenology trends in these areas. The spatial and temporal variability of phenological signals may provide a quantitative solution to the problem of how to evaluate dryland land surface trends across time.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute (MDPI)","doi":"10.3390/rs70810832","usgsCitation":"Walker, J.J., de Beurs, K., and Wynne, R., 2015, Phenological response of an Arizona dryland forest to short-term climatic extremes: Remote Sensing, v. 7, no. 8, p. 10832-10855, https://doi.org/10.3390/rs70810832.","productDescription":"24 p.","startPage":"10832","endPage":"10855","ipdsId":"IP-063470","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":472391,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70810832","text":"Publisher Index Page"},{"id":346919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.25,\n 34.5\n ],\n [\n -111.25,\n 34.5\n ],\n [\n -111.25,\n 35.5\n ],\n [\n -112.25,\n 35.5\n ],\n [\n -112.25,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-24","publicationStatus":"PW","scienceBaseUri":"59e8683ce4b05fe04cd4d241","contributors":{"authors":[{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Beurs, Kirsten","contributorId":197460,"corporation":false,"usgs":false,"family":"de Beurs","given":"Kirsten","email":"","affiliations":[],"preferred":false,"id":713534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wynne, Randolph","contributorId":197461,"corporation":false,"usgs":false,"family":"Wynne","given":"Randolph","email":"","affiliations":[],"preferred":false,"id":713535,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192019,"text":"70192019 - 2015 - Climate tolerances and habitat requirements jointly shape the elevational distribution of the American Pika (Ochotona princeps), with implications for climate change effects","interactions":[],"lastModifiedDate":"2017-10-26T14:06:24","indexId":"70192019","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Climate tolerances and habitat requirements jointly shape the elevational distribution of the American Pika (Ochotona princeps), with implications for climate change effects","title":"Climate tolerances and habitat requirements jointly shape the elevational distribution of the American Pika (Ochotona princeps), with implications for climate change effects","docAbstract":"Some of the most compelling examples of ecological responses to climate change are elevational range shifts of individual species, which have been observed throughout the world. A growing body of evidence, however, suggests substantial mediation of simple range shifts due to climate change by other limiting factors. Understanding limiting factors for a species within different contexts, therefore, is critical for predicting responses to climate change. The American pika (Ochotona princeps) is an ideal species for investigating distributions in relation to climate because of their unusual and well-understood natural history as well as observed shifts to higher elevation in parts of their range. We tested three hypotheses for the climatic or habitat characteristics that may limit pika presence and abundance: summer heat, winter snowpack, and forage availability. We performed these tests using an index of pika abundance gathered in a region where environmental influences on pika distribution have not been well-characterized. We estimated relative pika abundance via scat surveys and quantified climatic and habitat characteristics across two North-Central Rocky Mountain Ranges, the Wind River and Bighorn ranges in Wyoming, USA. Pika scat density was highest at mid-elevations and increased linearly with forage availability in both ranges. Scat density also increased with temperatures conducive to forage plant growth, and showed a unimodal relationship with the number of days below -5°C, which is modulated by insulating snowpack. Our results provide support for both the forage availability and winter snowpack hypotheses. Especially in montane systems, considering the context-dependent nature of climate effects across regions and elevations as well as interactions between climatic and other critical habitat characteristics, will be essential for predicting future species distributions.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0131082","usgsCitation":"Yandow, L.H., Chalfoun, A.D., and Doak, D.F., 2015, Climate tolerances and habitat requirements jointly shape the elevational distribution of the American Pika (Ochotona princeps), with implications for climate change effects: PLoS ONE, v. 10, no. 8, p. 1-21, https://doi.org/10.1371/journal.pone.0131082.","productDescription":"e0131082; 21 p.","startPage":"1","endPage":"21","ipdsId":"IP-043028","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":472434,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0131082","text":"Publisher Index Page"},{"id":347485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-05","publicationStatus":"PW","scienceBaseUri":"5a07eb8ce4b09af898c8ccf8","contributors":{"authors":[{"text":"Yandow, Leah H.","contributorId":198568,"corporation":false,"usgs":false,"family":"Yandow","given":"Leah","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":716422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":713850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doak, Daniel F.","contributorId":46811,"corporation":false,"usgs":true,"family":"Doak","given":"Daniel","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":716423,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189458,"text":"70189458 - 2015 - Incorporating temporal variation in seabird telemetry data: time variant kernel density models","interactions":[],"lastModifiedDate":"2017-07-14T10:20:39","indexId":"70189458","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Incorporating temporal variation in seabird telemetry data: time variant kernel density models","docAbstract":"A key component of the Mid-Atlantic Baseline Studies project was tracking the individual movements of focal marine bird species (Red-throated Loon [Gavia stellata], Northern Gannet [Morus bassanus], and Surf Scoter [Melanitta perspicillata]) through the use of satellite telemetry. This element of the project was a collaborative effort with the Department of Energy (DOE), Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (USFWS), and Sea Duck Joint Venture (SDJV), among other organizations. Satellite telemetry is an effective and informative tool for understanding individual animal movement patterns, allowing researchers to mark an individual once, and thereafter follow the movements of the animal in space and time. Aggregating telemetry data from multiple individuals can provide information about the spatial use and temporal movements of populations.
Tracking data is three dimensional, with the first two dimensions, X and Y, ordered along the third dimension, time. GIS software has many capabilities to store, analyze and visualize the location information, but little or no support for visualizing the temporal data, and tools for processing temporal data are lacking. We explored several ways of analyzing the movement patterns using the spatiotemporal data provided by satellite tags. Here, we present the results of one promising method: time-variant kernel density analysis (Keating and Cherry, 2009). The goal of this chapter is to demonstrate new methods in spatial analysis to visualize and interpret tracking data for a large number of individual birds across time in the mid-Atlantic study area and beyond. In this chapter, we placed greater emphasis on analytical methods than on the behavior and ecology of the animals tracked. For more detailed examinations of the ecology and wintering habitat use of the focal species in the midAtlantic, see Chapters 20-22.
","language":"English","publisher":"Biodiversity Research Institute","usgsCitation":"Gilbert, A., Adams, E.M., Anderson, C., Berlin, A., Bowman, T.D., Connelly, E., Gilliland, S., Gray, C., Lepage, C., Meattey, D., Montevecchi, W., Osenkowski, J., Savoy, L., Stenhouse, I., and Williams, K., 2015, Incorporating temporal variation in seabird telemetry data: time variant kernel density models, 21 p.","productDescription":"21 p.","startPage":"1","endPage":"21","ipdsId":"IP-085758","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":343847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343786,"type":{"id":15,"text":"Index Page"},"url":"https://www.briloon.org/"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5969d82ee4b0d1f9f060a1aa","contributors":{"authors":[{"text":"Gilbert, Andrew","contributorId":194560,"corporation":false,"usgs":false,"family":"Gilbert","given":"Andrew","affiliations":[],"preferred":false,"id":704652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Evan M.","contributorId":139994,"corporation":false,"usgs":false,"family":"Adams","given":"Evan","email":"","middleInitial":"M.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":704653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Carl","contributorId":194561,"corporation":false,"usgs":false,"family":"Anderson","given":"Carl","affiliations":[],"preferred":false,"id":704654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berlin, Alicia 0000-0002-5275-3077 aberlin@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":168416,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","email":"aberlin@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":704651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowman, Timothy D.","contributorId":80779,"corporation":false,"usgs":false,"family":"Bowman","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":704655,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Connelly, Emily","contributorId":194562,"corporation":false,"usgs":false,"family":"Connelly","given":"Emily","email":"","affiliations":[],"preferred":false,"id":704656,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gilliland, Scott","contributorId":194563,"corporation":false,"usgs":false,"family":"Gilliland","given":"Scott","affiliations":[],"preferred":false,"id":704657,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gray, Carrie E.","contributorId":127669,"corporation":false,"usgs":false,"family":"Gray","given":"Carrie E.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false},{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":704658,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lepage, Christine","contributorId":194564,"corporation":false,"usgs":false,"family":"Lepage","given":"Christine","email":"","affiliations":[],"preferred":false,"id":704659,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meattey, Dustin","contributorId":194565,"corporation":false,"usgs":false,"family":"Meattey","given":"Dustin","affiliations":[],"preferred":false,"id":704660,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Montevecchi, William","contributorId":171895,"corporation":false,"usgs":false,"family":"Montevecchi","given":"William","affiliations":[{"id":26965,"text":"Memorial University of Newfoundland","active":true,"usgs":false}],"preferred":false,"id":704661,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Osenkowski, Jason","contributorId":194566,"corporation":false,"usgs":false,"family":"Osenkowski","given":"Jason","affiliations":[],"preferred":false,"id":704662,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":704663,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stenhouse, Iain","contributorId":194567,"corporation":false,"usgs":false,"family":"Stenhouse","given":"Iain","affiliations":[],"preferred":false,"id":704664,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Williams, Kathryn","contributorId":194568,"corporation":false,"usgs":false,"family":"Williams","given":"Kathryn","affiliations":[],"preferred":false,"id":704665,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70192722,"text":"70192722 - 2015 - Biodiversity influences plant productivity through niche–efficiency","interactions":[],"lastModifiedDate":"2017-11-08T13:45:31","indexId":"70192722","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Biodiversity influences plant productivity through niche–efficiency","docAbstract":"The loss of biodiversity is threatening ecosystem productivity and services worldwide, spurring efforts to quantify its effects on the functioning of natural ecosystems. Previous research has focused on the positive role of biodiversity on resource acquisition (i.e., niche complementarity), but a lack of study on resource utilization efficiency, a link between resource and productivity, has rendered it difficult to quantify the biodiversity–ecosystem functioning relationship. Here we demonstrate that biodiversity loss reduces plant productivity, other things held constant, through theory, empirical evidence, and simulations under gradually relaxed assumptions. We developed a theoretical model named niche–efficiency to integrate niche complementarity and a heretofore-ignored mechanism of diminishing marginal productivity in quantifying the effects of biodiversity loss on plant productivity. Based on niche–efficiency, we created a relative productivity metric and a productivity impact index (PII) to assist in biological conservation and resource management. Relative productivity provides a standardized measure of the influence of biodiversity on individual productivity, and PII is a functionally based taxonomic index to assess individual species’ inherent value in maintaining current ecosystem productivity. Empirical evidence from the Alaska boreal forest suggests that every 1% reduction in overall plant diversity could render an average of 0.23% decline in individual tree productivity. Out of the 283 plant species of the region, we found that large woody plants generally have greater PII values than other species. This theoretical model would facilitate the integration of biological conservation in the international campaign against several pressing global issues involving energy use, climate change, and poverty.
","language":"English","publisher":"National Academy of Sciences of the United States of America","doi":"10.1073/pnas.1409853112","usgsCitation":"Liang, J., Zhou, M., Tobin, P.C., McGuire, A.D., and Reich, P., 2015, Biodiversity influences plant productivity through niche–efficiency: Proceedings of the National Academy of Sciences of the United States of America, v. 112, no. 18, p. 5738-5743, https://doi.org/10.1073/pnas.1409853112.","productDescription":"6 p.","startPage":"5738","endPage":"5743","ipdsId":"IP-049472","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":472444,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1409853112","text":"Publisher Index Page"},{"id":348464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","issue":"18","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-21","publicationStatus":"PW","scienceBaseUri":"5a0425c4e4b0dc0b45b4540f","contributors":{"authors":[{"text":"Liang, Jingjing","contributorId":189197,"corporation":false,"usgs":false,"family":"Liang","given":"Jingjing","email":"","affiliations":[],"preferred":false,"id":721281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Mo","contributorId":189200,"corporation":false,"usgs":false,"family":"Zhou","given":"Mo","email":"","affiliations":[],"preferred":false,"id":721282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tobin, Patrick C.","contributorId":200172,"corporation":false,"usgs":false,"family":"Tobin","given":"Patrick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":721283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":716773,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reich, Peter B.","contributorId":75835,"corporation":false,"usgs":true,"family":"Reich","given":"Peter B.","affiliations":[],"preferred":false,"id":721284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188039,"text":"70188039 - 2015 - Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)","interactions":[],"lastModifiedDate":"2017-05-30T16:05:47","indexId":"70188039","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5202,"text":"Journal of Applied Meteorology and Climatology","onlineIssn":"1558-8432","printIssn":"1558-8424","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)","docAbstract":"The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JAMC-D-14-0048.1","usgsCitation":"Tadesse, T., Wardlow, B.D., Brown, J.F., Svoboda, M., Hayes, M., Fuchs, B., and Gutzmer, D., 2015, Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI): Journal of Applied Meteorology and Climatology, v. 54, p. 153-169, https://doi.org/10.1175/JAMC-D-14-0048.1.","productDescription":"17 p.","startPage":"153","endPage":"169","ipdsId":"IP-057569","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84bee4b092b266f10d58","contributors":{"authors":[{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":696293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":696294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":3241,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696292,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Svoboda, Mark","contributorId":192357,"corporation":false,"usgs":false,"family":"Svoboda","given":"Mark","email":"","affiliations":[],"preferred":false,"id":696295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Michael","contributorId":192358,"corporation":false,"usgs":false,"family":"Hayes","given":"Michael","affiliations":[],"preferred":false,"id":696296,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuchs, Brian","contributorId":192359,"corporation":false,"usgs":false,"family":"Fuchs","given":"Brian","email":"","affiliations":[],"preferred":false,"id":696297,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gutzmer, Denise","contributorId":192360,"corporation":false,"usgs":false,"family":"Gutzmer","given":"Denise","email":"","affiliations":[],"preferred":false,"id":696298,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70189209,"text":"70189209 - 2015 - Processes limiting depth of arroyo incision: Examples from the Rio Puerco, New Mexico","interactions":[],"lastModifiedDate":"2017-07-06T15:38:26","indexId":"70189209","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Processes limiting depth of arroyo incision: Examples from the Rio Puerco, New Mexico","docAbstract":"We examined channel flow and suspended sediment transport processes within the lower Rio Puerco arroyo, located in semi-arid north-central New Mexico, in an attempt to answer the question: Why did arroyo incision stop by about 1936? Channel flow model results show that in the narrow, incised channel of 1936, the boundary shear stress during a large flood was highest over the lower banks and bank toes, causing a higher potential for erosion of these surfaces than of the channel bed. This would have caused the channel (and arroyo) to widen, and the higher sediment fluxes from those surfaces would have inhibited the capacity of the flow to erode the bed. We found that volumes of sediment delivered to the channel from local erosion of the arroyo wall did not exceed the capacity of the flow to transport sediment, including sand, in suspension. However, sediment supplied from erosion upstream of our study reach may have reduced the capacity of the flow to erode the bed. Our results suggest that arroyo incision ended with the observed reduction in flood peak magnitude, frequency, and duration after 1941.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, Nevada","language":"English","publisher":"ACWI Subcommittee on Sedimentation and Subcommittee on Hydrology","usgsCitation":"Griffin, E.R., and Friedman, J.M., 2015, Processes limiting depth of arroyo incision: Examples from the Rio Puerco, New Mexico, in Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference, Reno, Nevada, April 19-23, 2015, p. 797-808.","productDescription":"12 p.","startPage":"797","endPage":"808","ipdsId":"IP-061060","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343368,"type":{"id":15,"text":"Index Page"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"595f4c41e4b0d1f9f057e35a","contributors":{"authors":[{"text":"Griffin, Eleanor R. 0000-0001-6724-9853 egriffin@usgs.gov","orcid":"https://orcid.org/0000-0001-6724-9853","contributorId":1775,"corporation":false,"usgs":true,"family":"Griffin","given":"Eleanor","email":"egriffin@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":703507,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189465,"text":"70189465 - 2015 - Identifying sediment sources in the sediment TMDL process","interactions":[],"lastModifiedDate":"2017-07-13T13:10:35","indexId":"70189465","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Identifying sediment sources in the sediment TMDL process","docAbstract":"Sediment is an important pollutant contributing to aquatic-habitat degradation in many waterways of the United States. This paper discusses the application of sediment budgets in conjunction with sediment fingerprinting as tools to determine the sources of sediment in impaired waterways. These approaches complement monitoring, assessment, and modeling of sediment erosion, transport, and storage in watersheds. Combining the sediment fingerprinting and sediment budget approaches can help determine specific adaptive management plans and techniques applied to targeting hot spots or areas of high erosion.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 3rd Joint Federal Interagency Conference (10th Federal Interagency Sedimentation Conference and 5th Federal Interagency Hydrologic Modeling Conference)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Proceedings of the 3rd Joint Federal Interagency Conference (10th Federal Interagency Sedimentation Conference and 5th Federal Interagency Hydrologic Modeling Conference)","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, VA","language":"English","usgsCitation":"Gellis, A., Fitzpatrick, F., Schubauer-Berigan, J.P., Landy, R., and Gorman Sanisaca, L., 2015, Identifying sediment sources in the sediment TMDL process, in Proceedings of the 3rd Joint Federal Interagency Conference (10th Federal Interagency Sedimentation Conference and 5th Federal Interagency Hydrologic Modeling Conference), Reno, VA, April 19-23, 2015, p. 1983-1991.","productDescription":"9 p.","startPage":"1983","endPage":"1991","ipdsId":"IP-062527","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":343799,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/Proceedings.pdf"},{"id":343800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596886a2e4b0d1f9f05f59ca","contributors":{"authors":[{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":1709,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen C.","email":"agellis@usgs.gov","affiliations":[{"id":375,"text":"Maryland, Delaware, and the District of Columbia Water Science Center","active":false,"usgs":true}],"preferred":false,"id":704787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":173463,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","email":"fafitzpa@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":704788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schubauer-Berigan, Joseph P.","contributorId":106220,"corporation":false,"usgs":true,"family":"Schubauer-Berigan","given":"Joseph","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":704789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landy, R.B.","contributorId":101360,"corporation":false,"usgs":true,"family":"Landy","given":"R.B.","email":"","affiliations":[],"preferred":false,"id":704790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864 lgormansanisaca@usgs.gov","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":172247,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian E.","email":"lgormansanisaca@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":false,"id":704791,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189910,"text":"70189910 - 2015 - PhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQC","interactions":[],"lastModifiedDate":"2017-08-03T14:32:23","indexId":"70189910","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"PhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQC","docAbstract":"PhreeqcRM is a geochemical reaction module designed specifically to perform equilibrium and kinetic reaction calculations for reactive transport simulators that use an operator-splitting approach. The basic function of the reaction module is to take component concentrations from the model cells of the transport simulator, run geochemical reactions, and return updated component concentrations to the transport simulator. If multicomponent diffusion is modeled (e.g., Nernst–Planck equation), then aqueous species concentrations can be used instead of component concentrations. The reaction capabilities are a complete implementation of the reaction capabilities of PHREEQC. In each cell, the reaction module maintains the composition of all of the reactants, which may include minerals, exchangers, surface complexers, gas phases, solid solutions, and user-defined kinetic reactants.
PhreeqcRM assigns initial and boundary conditions for model cells based on standard PHREEQC input definitions (files or strings) of chemical compositions of solutions and reactants. Additional PhreeqcRM capabilities include methods to eliminate reaction calculations for inactive parts of a model domain, transfer concentrations and other model properties, and retrieve selected results. The module demonstrates good scalability for parallel processing by using multiprocessing with MPI (message passing interface) on distributed memory systems, and limited scalability using multithreading with OpenMP on shared memory systems. PhreeqcRM is written in C++, but interfaces allow methods to be called from C or Fortran. By using the PhreeqcRM reaction module, an existing multicomponent transport simulator can be extended to simulate a wide range of geochemical reactions. Results of the implementation of PhreeqcRM as the reaction engine for transport simulators PHAST and FEFLOW are shown by using an analytical solution and the reactive transport benchmark of MoMaS.
In 1992, a 356-mm minimum length limit (MLL) was enacted on Kentucky Lake and a 381-mm MLL was enacted on Watts Bar Lake, two mainstem reservoirs on the Tennessee River, in an attempt to reduce exploitation and improve the size structure of the sauger (Sander canadensis) populations. The objectives of this study were to compare sauger population characteristics immediately following (1993–1994) and 15 years after (2008–2009) the regulations took effect, examine spatial and temporal patterns in growth, examine recruitment patterns in each reservoir using a recruitment variability index (RVI), and assess the current likelihood of overfishing. Saugers were collected with experimental gill nets in each reservoir and aged using otoliths. A Beverton-Holt yield-per-recruit model was used to simulate angler yields and estimate the likelihood of growth overfishing. Recruitment overfishing was assessed by examining spawning potential ratios under various MLL and exploitation rate scenarios. The sauger population in Kentucky Lake experienced modest improvements in size and age structure over the 15 years following enactment of more restrictive harvest regulations, whereas the population in Watts Bar Lake changed very little, if at all, in terms of size and age structure. Mean lengths of age-3 sauger were significantly greater in Watts Bar Lake than in Kentucky Lake in both time periods. The RVI values indicated that between 1993 and 2009 the sauger in Kentucky Lake displayed more stable recruitment than the Watts Bar Lake population. Neither population exhibited signs of growth overfishing in 2008–09 under the current length limits; however, the Watts Bar Lake population would be susceptible to recruitment overfishing at high (>40%) exploitation rates if natural mortality was as low as 20%. These analyses have demonstrated that the Watts Bar Lake and Kentucky Lake populations, in terms of size and age structure, have remained relatively stable over 15+ years and the MLLs appear to be conserving the stocks.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Graham, C.L., Bettoli, P.W., and Churchill, T.N., 2015, An assessment of sauger population characteristics on two Tennessee River reservoirs: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 2, p. 101-108.","productDescription":"8 p.","startPage":"101","endPage":"108","ipdsId":"IP-057497","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":347455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347454,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.seafwa.org/publications/journal/?id=94"}],"country":"United States","otherGeospatial":"Tennessee River","volume":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07eb8ce4b09af898c8ccf4","contributors":{"authors":[{"text":"Graham, Christy L.","contributorId":198476,"corporation":false,"usgs":false,"family":"Graham","given":"Christy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":716188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bettoli, Phillip William pbettoli@usgs.gov","contributorId":1919,"corporation":false,"usgs":true,"family":"Bettoli","given":"Phillip","email":"pbettoli@usgs.gov","middleInitial":"William","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":716167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Churchill, Timothy N.","contributorId":190276,"corporation":false,"usgs":false,"family":"Churchill","given":"Timothy","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":716189,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188036,"text":"70188036 - 2015 - Automated integration of lidar into the LANDFIRE product suite","interactions":[],"lastModifiedDate":"2018-01-28T16:22:27","indexId":"70188036","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3251,"text":"Remote Sensing Letters","active":true,"publicationSubtype":{"id":10}},"title":"Automated integration of lidar into the LANDFIRE product suite","docAbstract":"Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation and fuel mapping process because spatially continuous lidar data are not available at the national scale. Second, while lidar data are available for many land management units across the US, these data are underutilized for fire behaviour applications. This is partly due to a lack of local personnel trained to process and analyse lidar data. This investigation addresses these issues by developing the Creating Hybrid Structure from LANDFIRE/lidar Combinations (CHISLIC) tool. CHISLIC allows individuals to automatically generate a suite of vegetation structure and wildland fuel parameters from lidar data and infuse them into existing LANDFIRE data sets. CHISLIC will become available for wider distribution to the public through a partnership with the U.S. Forest Service’s Wildland Fire Assessment System (WFAS) and may be incorporated into the Wildland Fire Decision Support System (WFDSS) with additional design and testing. WFAS and WFDSS are the primary systems used to support tactical and strategic wildland fire management decisions.
","language":"English","publisher":"Taylor & Frances","doi":"10.1080/2150704X.2015.1029086","usgsCitation":"Peterson, B., Nelson, K., Seielstad, C., Stoker, J.M., Jolly, W.M., and Parsons, R., 2015, Automated integration of lidar into the LANDFIRE product suite: Remote Sensing Letters, v. 6, no. 3, p. 247-256, https://doi.org/10.1080/2150704X.2015.1029086.","productDescription":"10 p.","startPage":"247","endPage":"256","ipdsId":"IP-057258","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341894,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-30","publicationStatus":"PW","scienceBaseUri":"592e84bee4b092b266f10d5d","contributors":{"authors":[{"text":"Peterson, Birgit 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":192353,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seielstad, Carl","contributorId":192354,"corporation":false,"usgs":false,"family":"Seielstad","given":"Carl","email":"","affiliations":[],"preferred":false,"id":696286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696287,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jolly, W. Matt","contributorId":192355,"corporation":false,"usgs":false,"family":"Jolly","given":"W.","email":"","middleInitial":"Matt","affiliations":[],"preferred":false,"id":696288,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parsons, Russell","contributorId":192356,"corporation":false,"usgs":false,"family":"Parsons","given":"Russell","affiliations":[],"preferred":false,"id":696289,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189826,"text":"70189826 - 2015 - Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities","interactions":[],"lastModifiedDate":"2017-07-27T16:10:12","indexId":"70189826","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities","docAbstract":"Prior to calculating time-independent and -dependent earthquake probabilities for faults in the Wasatch Front region, the Working Group on Utah Earthquake Probabilities (WGUEP) updated a seismic-source model for the region (Wong and others, 2014) and evaluated 19 historical regressions on earthquake magnitude (M). These regressions relate M to fault parameters for historical surface-faulting earthquakes, including linear fault length (e.g., surface-rupture length [SRL] or segment length), average displacement, maximum displacement, rupture area, seismic moment (Mo ), and slip rate. These regressions show that significant epistemic uncertainties complicate the determination of characteristic magnitude for fault sources in the Basin and Range Province (BRP). For example, we found that M estimates (as a function of SRL) span about 0.3–0.4 units (figure 1) owing to differences in the fault parameter used; age, quality, and size of historical earthquake databases; and fault type and region considered.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Basin and Range Province Seismic Hazards Summit III, Utah Geological Survey Miscellaneous Publication 15-5","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"conferenceTitle":"Basin and Range Province Seismic Hazards Summit III","language":"English","publisher":"Utah Geological Survey","usgsCitation":"DuRoss, C., Olig, S., and Schwartz, D., 2015, Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities, in Basin and Range Province Seismic Hazards Summit III, Utah Geological Survey Miscellaneous Publication 15-5, 30 p.","productDescription":"30 p.","ipdsId":"IP-064153","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":344373,"type":{"id":15,"text":"Index Page"},"url":"https://ugspub.nr.utah.gov/publications/misc_pubs/mp-15-5/mp-15-5_technical_sessions1-2.pdf"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597afba7e4b0a38ca2750b6a","contributors":{"authors":[{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":706480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olig, Susan","contributorId":195184,"corporation":false,"usgs":false,"family":"Olig","given":"Susan","affiliations":[],"preferred":false,"id":706481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwartz, David","contributorId":195185,"corporation":false,"usgs":false,"family":"Schwartz","given":"David","affiliations":[],"preferred":false,"id":706482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189623,"text":"70189623 - 2015 - Numerical modeling of injection, stress and permeability enhancement during shear stimulation at the Desert Peak Enhanced Geothermal System","interactions":[],"lastModifiedDate":"2017-07-19T10:43:46","indexId":"70189623","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2070,"text":"International Journal of Rock Mechanics and Mining Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Numerical modeling of injection, stress and permeability enhancement during shear stimulation at the Desert Peak Enhanced Geothermal System","docAbstract":"Creation of an Enhanced Geothermal System relies on stimulation of fracture permeability through self-propping shear failure that creates a complex fracture network with high surface area for efficient heat transfer. In 2010, shear stimulation was carried out in well 27-15 at Desert Peak geothermal field, Nevada, by injecting cold water at pressure less than the minimum principal stress. An order-of-magnitude improvement in well injectivity was recorded. Here, we describe a numerical model that accounts for injection-induced stress changes and permeability enhancement during this stimulation. In a two-part study, we use the coupled thermo-hydrological-mechanical simulator FEHM to: (i) construct a wellbore model for non-steady bottom-hole temperature and pressure conditions during the injection, and (ii) apply these pressures and temperatures as a source term in a numerical model of the stimulation. In this model, a Mohr-Coulomb failure criterion and empirical fracture permeability is developed to describe permeability evolution of the fractured rock. The numerical model is calibrated using laboratory measurements of material properties on representative core samples and wellhead records of injection pressure and mass flow during the shear stimulation. The model captures both the absence of stimulation at low wellhead pressure (WHP ≤1.7 and ≤2.4 MPa) as well as the timing and magnitude of injectivity rise at medium WHP (3.1 MPa). Results indicate that thermoelastic effects near the wellbore and the associated non-local stresses further from the well combine to propagate a failure front away from the injection well. Elevated WHP promotes failure, increases the injection rate, and cools the wellbore; however, as the overpressure drops off with distance, thermal and non-local stresses play an ongoing role in promoting shear failure at increasing distance from the well.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijrmms.2015.06.003","usgsCitation":"Dempsey, D., Kelkar, S., Davatzes, N., Hickman, S.H., and Moos, D., 2015, Numerical modeling of injection, stress and permeability enhancement during shear stimulation at the Desert Peak Enhanced Geothermal System: International Journal of Rock Mechanics and Mining Sciences, v. 78, p. 190-206, https://doi.org/10.1016/j.ijrmms.2015.06.003.","productDescription":"17 p.","startPage":"190","endPage":"206","ipdsId":"IP-065414","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":472392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1468563","text":"Publisher Index Page"},{"id":344012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.68530273437499,\n 39.884450178234395\n ],\n [\n -117.56469726562499,\n 39.884450178234395\n ],\n [\n -117.56469726562499,\n 40.6056120582602\n ],\n [\n -118.68530273437499,\n 40.6056120582602\n ],\n [\n -118.68530273437499,\n 39.884450178234395\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59706fbae4b0d1f9f065a8d4","contributors":{"authors":[{"text":"Dempsey, David","contributorId":194844,"corporation":false,"usgs":false,"family":"Dempsey","given":"David","email":"","affiliations":[],"preferred":false,"id":705475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelkar, Sharad","contributorId":194845,"corporation":false,"usgs":false,"family":"Kelkar","given":"Sharad","email":"","affiliations":[],"preferred":false,"id":705476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davatzes, Nick","contributorId":194846,"corporation":false,"usgs":false,"family":"Davatzes","given":"Nick","email":"","affiliations":[],"preferred":false,"id":705477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hickman, Stephen H. 0000-0003-2075-9615 hickman@usgs.gov","orcid":"https://orcid.org/0000-0003-2075-9615","contributorId":2705,"corporation":false,"usgs":true,"family":"Hickman","given":"Stephen","email":"hickman@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":705474,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moos, Daniel","contributorId":194847,"corporation":false,"usgs":false,"family":"Moos","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":705478,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}