Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110943","usgsCitation":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J.J., Howard, D., Smith, K., and Nelson, K., 2016, An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data: Remote Sensing, v. 8, p. 1-13, https://doi.org/10.3390/rs8110943.","productDescription":"Article 943; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-079805","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110943","text":"Publisher Index Page"},{"id":331008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfa","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176610,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":653758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":653760,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":653759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178355,"text":"70178355 - 2016 - Role of riparian shade on the fish assemblage of a reservoir littoral","interactions":[],"lastModifiedDate":"2016-11-15T12:00:05","indexId":"70178355","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Role of riparian shade on the fish assemblage of a reservoir littoral","docAbstract":"Research into the effects of shade on reservoir fish assemblages is lacking, with most investigations focused on streams. Unlike many streams, the canopy in a reservoir shades only a narrow fringe of water adjacent to the shoreline, and may not have the influential effect on the aquatic environment reported in streams. We compared fish assemblages between shaded and unshaded sites in a shallow reservoir. Overall species richness (gamma diversity) was higher in shaded sites, and fish assemblage composition differed between shaded and unshaded sites. Average light intensity was 66 % lower in shaded sites, and differences in average temperature and dissolved oxygen were small. Unlike streams where shade can have large effects on water physicochemistry, in reservoirs shade-related differences in fish assemblages seemed to be linked principally to differences in light intensity. Diversity in light intensity in shaded and unshaded sites in reservoirs can create various mosaics of light-based habitats that enable diversity of species assemblages. Managing to promote the habitat diversity provided by shade may require coping with the artificial nature of reservoir riparian zones and water level fluctuations.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-016-0519-4","usgsCitation":"Raines, C.D., and Miranda, L.E., 2016, Role of riparian shade on the fish assemblage of a reservoir littoral: Environmental Biology of Fishes, v. 99, no. 10, p. 753-760, https://doi.org/10.1007/s10641-016-0519-4.","productDescription":"8 p.","startPage":"753","endPage":"760","ipdsId":"IP-076309","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":331009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-19","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfc","contributors":{"authors":[{"text":"Raines, C. D.","contributorId":176859,"corporation":false,"usgs":false,"family":"Raines","given":"C.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":653819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":653753,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178373,"text":"70178373 - 2016 - Using structural equation modeling to link human activities to wetland ecological integrity","interactions":[],"lastModifiedDate":"2016-12-01T13:28:42","indexId":"70178373","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Using structural equation modeling to link human activities to wetland ecological integrity","docAbstract":"The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1548","usgsCitation":"Schweiger, E.W., Grace, J.B., Cooper, D., Bobowski, B., and Britten, M., 2016, Using structural equation modeling to link human activities to wetland ecological integrity: Ecosphere, v. 7, no. 11, p. 1-30, https://doi.org/10.1002/ecs2.1548.","productDescription":"e01548; 30 p.","startPage":"1","endPage":"30","ipdsId":"IP-074267","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470418,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1548","text":"Publisher Index Page"},{"id":331006,"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 -105.93292236328124,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.15998434802335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bf8","chorus":{"doi":"10.1002/ecs2.1548","url":"http://dx.doi.org/10.1002/ecs2.1548","publisher":"Wiley-Blackwell","authors":"Schweiger E. William, Grace James B., Cooper David, Bobowski Ben, Britten Mike","journalName":"Ecosphere","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"Schweiger, E. William","contributorId":53635,"corporation":false,"usgs":true,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":653814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":653815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, David","contributorId":176856,"corporation":false,"usgs":false,"family":"Cooper","given":"David","affiliations":[],"preferred":false,"id":653816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bobowski, Ben","contributorId":176857,"corporation":false,"usgs":false,"family":"Bobowski","given":"Ben","email":"","affiliations":[],"preferred":false,"id":653817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Mike","contributorId":176858,"corporation":false,"usgs":false,"family":"Britten","given":"Mike","email":"","affiliations":[],"preferred":false,"id":653818,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182812,"text":"70182812 - 2016 - Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA","interactions":[],"lastModifiedDate":"2017-03-01T10:36:16","indexId":"70182812","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA","docAbstract":"Many debris flows increase in volume as they travel downstream, enhancing their mobility and hazard. Volumetric growth can result from diverse physical processes, such as channel sediment entrainment, stream bank collapse, adjacent landsliding, hillslope erosion and rilling, and coalescence of multiple debris flows; incorporating these varied phenomena into physics-based debris-flow models is challenging. As an alternative, we embedded effects of debris-flow growth into an empirical/statistical approach to forecast potential inundation areas within digital landscapes in a GIS framework. Our approach used an empirical debris-growth function to account for the effects of growth phenomena. We applied this methodology to a debris-flow-prone area in the Oregon Coast Range, USA, where detailed mapping revealed areas of erosion and deposition along paths of debris flows that occurred during a large storm in 1996. Erosion was predominant in stream channels with slopes > 5°. Using pre- and post-event aerial photography, we derived upslope contributing area and channel-length growth factors. Our method reproduced the observed inundation patterns produced by individual debris flows; it also generated reproducible, objective potential inundation maps for entire drainage networks. These maps better matched observations than those using previous methods that focus on proximal or distal regions of a drainage network.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.geomorph.2016.07.039","usgsCitation":"Reid, M.E., Coe, J.A., and Brien, D., 2016, Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA: Geomorphology, v. 273, p. 396-411, https://doi.org/10.1016/j.geomorph.2016.07.039.","productDescription":"16 p. ","startPage":"396","endPage":"411","ipdsId":"IP-071225","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2016.07.039","text":"Publisher Index Page"},{"id":336725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"273","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baf1","contributors":{"authors":[{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":673853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":673854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brien, Dianne dbrien@usgs.gov","contributorId":176271,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":673855,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178337,"text":"70178337 - 2016 - Effects of climate and water balance across grasslands of varying C3 and C4 grass cover","interactions":[],"lastModifiedDate":"2016-11-14T12:30:15","indexId":"70178337","displayToPublicDate":"2016-11-14T13:20:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate and water balance across grasslands of varying C3 and C4 grass cover","docAbstract":"Climate change in grassland ecosystems may lead to divergent shifts in the abundance and distribution of C3 and C4 grasses. Many studies relate mean climate conditions over relatively long time periods to plant cover, but there is still much uncertainty about how the balance of C3and C4 species will be affected by climate at a finer temporal scale than season (individual events to months). We monitored cover at five grassland sites with co-dominant C3 and C4 grass species or only dominant C3 grass species for 6 yr in national parks across the Colorado Plateau region to assess the influence of specific months of climate and water balance on changes in grass cover. C4 grass cover increased and decreased to a larger degree than C3 grass cover with extremely dry and wet consecutive years, but this response varied by ecological site. Climate and water balance explained 10–49% of the inter-annual variability of cover of C3 and C4 grasses at all sites. High precipitation in the spring and in previous year monsoon storms influenced changes in cover of C4 grasses, with measures of water balance in the same months explaining additional variability. C3 grasses in grasslands where they were dominant were influenced primarily by longer periods of climate, while C3 grasses in grasslands where they were co-dominant with C4 grasses were influenced little by climate anomalies at either short or long periods of time. Our results suggest that future changes in spring and summer climate and water balance are likely to affect cover of both C3 and C4 grasses, but cover of C4 grasses may be affected more strongly, and the degree of change will depend on soils and topography where they are growing and the timing of the growing season.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1577","usgsCitation":"Witwicki, D.L., Munson, S.M., and Thoma, D.P., 2016, Effects of climate and water balance across grasslands of varying C3 and C4 grass cover: Ecosphere, v. 7, no. 11, e01577; 19 p., https://doi.org/10.1002/ecs2.1577.","productDescription":"e01577; 19 p.","ipdsId":"IP-074409","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470424,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1577","text":"Publisher Index Page"},{"id":330974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-08","publicationStatus":"PW","scienceBaseUri":"582adb41e4b0c253bdfff08c","chorus":{"doi":"10.1002/ecs2.1577","url":"http://dx.doi.org/10.1002/ecs2.1577","publisher":"Wiley-Blackwell","authors":"Witwicki Dana L., Munson Seth M., Thoma David P.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/15/2016"},"contributors":{"authors":[{"text":"Witwicki, Dana L.","contributorId":72473,"corporation":false,"usgs":true,"family":"Witwicki","given":"Dana","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":653649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":653650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thoma, David P.","contributorId":45975,"corporation":false,"usgs":true,"family":"Thoma","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":653651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178339,"text":"70178339 - 2016 - Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate","interactions":[],"lastModifiedDate":"2017-01-03T16:07:07","indexId":"70178339","displayToPublicDate":"2016-11-14T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate","docAbstract":"Understanding how aquatic-macroinvertebrate communities respond to changes in climate is important for biodiversity conservation in the Prairie Pothole Region and other wetland-rich landscapes. We sampled macroinvertebrate communities of 162 wetlands and lakes previously sampled from 1966 to 1976, a much drier period compared to our 2012–2013 sampling timeframe. To identify possible influences of a changed climate and predation pressures on macroinvertebrates, we compared two predictors of aquatic-macroinvertebrate communities: ponded-water dissolved-ion concentration and vertebrate-predator presence/abundance. Further, we make inferences of how macroinvertebrate communities were structured during the drier period when the range of dissolved-ion concentrations was much greater and fish occurrence in aquatic habitats was rare. We found that aquatic-macroinvertebrate community structure was influenced by dissolved-ion concentrations through a complex combination of direct and indirect relationships. Ion concentrations also influenced predator occurrence and abundance, which indirectly affected macroinvertebrate communities. It is important to consider both abiotic and biotic gradients when predicting how invertebrate communities will respond to climate change. Generally, in the wetlands and lakes we studied, freshening of ponded water resulted in more homogenous communities than occurred during a much drier period when salinity range among sites was greater.
","language":"English","publisher":"Wetlands","doi":"10.1007/s13157-016-0848-2","usgsCitation":"McLean, K.I., Mushet, D.M., Renton, D., and Stockwell, C., 2016, Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate: Wetlands, v. 36, no. s2, p. 423-435, https://doi.org/10.1007/s13157-016-0848-2.","productDescription":"13 p.","startPage":"423","endPage":"435","ipdsId":"IP-071577","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582adb43e4b0c253bdfff094","contributors":{"authors":[{"text":"McLean, Kyle I. kmclean@usgs.gov","contributorId":147397,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","middleInitial":"I.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":653645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Renton, David A. drenton@usgs.gov","contributorId":138600,"corporation":false,"usgs":true,"family":"Renton","given":"David A.","email":"drenton@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":653647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockwell, Craig A.","contributorId":55257,"corporation":false,"usgs":true,"family":"Stockwell","given":"Craig A.","affiliations":[],"preferred":false,"id":653648,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178340,"text":"70178340 - 2016 - Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","interactions":[],"lastModifiedDate":"2016-11-14T12:17:49","indexId":"70178340","displayToPublicDate":"2016-11-14T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","docAbstract":"The Delaware River Basin (DRB) encompasses approximately 0.4 % of the area of the United States (U.S.), but supplies water to 5 % of the population. We studied three forested tributaries to quantify the potential climate-driven change in hydrologic budget for two 25-year time periods centered on 2030 and 2060, focusing on sensitivity to the method of estimating potential evapotranspiration (PET) change. Hydrology was simulated using the Water Availability Tool for Environmental Resources (Williamson et al. 2015). Climate-change scenarios for four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and two Representative Concentration Pathways (RCPs) were used to derive monthly change factors for temperature (T), precipitation (PPT), and PET according to the energy-based method of Priestley and Taylor (1972). Hydrologic simulations indicate a general increase in annual (especially winter) streamflow (Q) as early as 2030 across the DRB, with a larger increase by 2060. This increase in Q is the result of (1) higher winter PPT, which outweighs an annual actual evapotranspiration (AET) increase and (2) (for winter) a major shift away from storage of PPT as snow pack. However, when PET change is evaluated instead using the simpler T-based method of Hamon (1963), the increases in Q are small or even negative. In fact, the change of Q depends as much on PET method as on time period or RCP. This large sensitivity and associated uncertainty underscore the importance of exercising caution in the selection of a PET method for use in climate-change analyses.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-016-1782-2","usgsCitation":"Williamson, T., Nystrom, E.A., and Milly, P., 2016, Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration: Climatic Change, v. 139, no. 2, p. 215-228, https://doi.org/10.1007/s10584-016-1782-2.","productDescription":"14 p.","startPage":"215","endPage":"228","ipdsId":"IP-072262","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":330971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-16","publicationStatus":"PW","scienceBaseUri":"582adb44e4b0c253bdfff09a","chorus":{"doi":"10.1007/s10584-016-1782-2","url":"http://dx.doi.org/10.1007/s10584-016-1782-2","publisher":"Springer Nature","authors":"Williamson Tanja N., Nystrom Elizabeth A., Milly Paul C. D.","journalName":"Climatic Change","publicationDate":"9/16/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/16/2016"},"contributors":{"authors":[{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":148942,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":653642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":653644,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177933,"text":"fs20163081 - 2016 - Beryllium—A critical mineral commodity—Resources, production, and supply chain","interactions":[],"lastModifiedDate":"2018-10-22T09:08:22","indexId":"fs20163081","displayToPublicDate":"2016-11-14T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3081","title":"Beryllium—A critical mineral commodity—Resources, production, and supply chain","docAbstract":"Beryllium is a lightweight metallic element used in a wide variety of specialty and industrial applications. As a function of its unique chemical and physical properties, such as a high stiffness-to-weight ratio, resistance to temperature extremes, and high thermal conductivity, beryllium cannot be easily replaced by substitute materials in applications where combinations of these properties make it the material of choice. Because the number of beryllium producers is limited and the use of substitute materials in specific defense-related applications that are vital to national security is inadequate, several studies have categorized beryllium as a critical and strategic material. This categorization has led to the United States Government recommending that beryllium be stockpiled for use in the event of a national emergency. As of December 31, 2015, the National Defense Stockpile inventory of hot-pressed beryllium metal powder, structured beryllium metal powder, and vacuum-cast beryllium metal totaled 78 metric tons (t).
The U.S. Geological Survey (USGS) Mineral Resources Program supports research on the occurrence, quality, quantity, and availability of mineral resources vital to the economy and national security. The USGS, through its National Minerals Information Center (NMIC), collects, analyzes, and disseminates information on more than 90 nonfuel mineral commodities from more than 180 countries. This fact sheet provides information on the production, consumption, supply chain, geology, and resource availability of beryllium in a global context.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163081","usgsCitation":"Lederer, G.W., Foley, N.K., Jaskula, B.W., and Ayuso, R.A., 2016, Beryllium—A critical mineral commodity—Resources, production, and supply chain: U.S. Geological Survey Fact Sheet 2016–3081, 4 p., https://dx.doi.org/10.3133/fs20163081.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-077851","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":330523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3081/coverthb3.jpg"},{"id":330524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3081/fs20163081.pdf","text":"Report","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3081"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Or visit the USGS Minerals Information Web site at: http://minerals.usgs.gov/minerals
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2015 (October 1, 2014, through September 30, 2015), data were collected at 74 stations—72 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 71 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1023","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., and Heimann, D.C., 2016, Quality of surface water in Missouri, water year 2015: U.S. Geological Survey Data Series 1023, 22 p., https://dx.doi.org/10.3133/ds1023.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-077875","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":330865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1023/ds1023.pdf","text":"Report","size":"5.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1023"},{"id":330864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1023/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (Charadrius melodus) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (n = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0164979","usgsCitation":"Thieler, E.R., Zeigler, S.L., Winslow, L., Hines, M., Read, J.S., and Walker, J.I., 2016, Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability: PLoS ONE, v. 11, no. 11, e0164979; 22 p., https://doi.org/10.1371/journal.pone.0164979.","productDescription":"e0164979; 22 p.","ipdsId":"IP-077649","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470425,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164979","text":"Publisher Index Page"},{"id":330973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582adb45e4b0c253bdfff0a5","contributors":{"authors":[{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara L. 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":169601,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":653637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, Megan 0000-0002-9845-4849 mhines@usgs.gov","orcid":"https://orcid.org/0000-0002-9845-4849","contributorId":4783,"corporation":false,"usgs":true,"family":"Hines","given":"Megan","email":"mhines@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":653639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jordan I. 0000-0003-2226-3373 jiwalker@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-3373","contributorId":4608,"corporation":false,"usgs":true,"family":"Walker","given":"Jordan","email":"jiwalker@usgs.gov","middleInitial":"I.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653640,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178726,"text":"70178726 - 2016 - Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling","interactions":[],"lastModifiedDate":"2017-02-02T11:01:52","indexId":"70178726","displayToPublicDate":"2016-11-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling","docAbstract":"There is considerable debate about the role of competition in shaping species distributions over broad spatial extents. This debate has practical implications because predicting changes in species' geographic ranges in response to ongoing environmental change would be simpler if competition could be ignored. While this debate has been the subject of many reviews, recent literature has not addressed the rates of relevant processes. This omission is surprising in that ecologists hypothesized decades ago that regional competitive exclusion is a slow process. The goal of this review is to reassess the debate under the hypothesis that competitive exclusion over broad spatial extents is a slow process.
Available evidence, including simulations presented for the first time here, suggests that competitive exclusion over broad spatial extents occurs slowly over temporal extents of many decades to millennia. Ecologists arguing against an important role for competition frequently study modern patterns and/or range dynamics over periods of decades, while much of the evidence for competition shaping geographic ranges at broad spatial extents comes from paleoecological studies over time scales of centuries or longer. If competition is slow, as evidence suggests, the geographic distributions of some, perhaps many species, would continue to change over time scales of decades to millennia, even if environmental conditions did not continue to change. If the distributions of competing species are at equilibrium it is possible to predict species distributions based on observed species–environment relationships. However, disequilibrium is widespread as a result of competition and many other processes. Studies whose goal is accurate predictions over intermediate time scales (decades to centuries) should focus on factors associated with range expansion (colonization) and loss (local extinction), as opposed to current patterns. In general, understanding of modern range dynamics would be enhanced by considering the rates of relevant processes.
","language":"English","publisher":"Blackwell Publishers ","doi":"10.1111/ecog.02836","usgsCitation":"Yackulic, C.B., 2016, Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling: Ecography, v. 40, no. 2, p. 305-313, https://doi.org/10.1111/ecog.02836.","productDescription":"9 p.","startPage":"305","endPage":"313","ipdsId":"IP-073460","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.02836","text":"Publisher Index Page"},{"id":331661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"58492df2e4b06d80b7b093a2","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":654997,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70177049,"text":"fs20163083 - 2016 - Assessment of undiscovered continuous oil and shale-gas resources in the Bazhenov Formation of the West Siberian Basin Province, Russia, 2016","interactions":[],"lastModifiedDate":"2018-02-15T14:59:47","indexId":"fs20163083","displayToPublicDate":"2016-11-10T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3083","title":" Assessment of undiscovered continuous oil and shale-gas resources in the Bazhenov Formation of the West Siberian Basin Province, Russia, 2016","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean continuous resources of 12 billion barrels of oil and 75 trillion cubic feet of gas in the Bazhenov Formation of the West Siberian Basin Province, Russia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163083","usgsCitation":"Klett, T.R., Schenk, C.J., Brownfield, M.E., Leathers-Miller, H.M., Mercier, T.J., Pitman, J.K., and Tennyson, M.E., 2016, Assessment of undiscovered continuous oil and shale-gas resources in the Bazhenov Formation of the West Siberian Basin Province, Russia, 2016: U.S. Geological Survey Fact Sheet 2016–3083, 2 p., https://dx.doi.org/10.3133/fs20163083.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-076584","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":330918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3083/coverthb2.jpg"},{"id":330919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3083/fs20163083.pdf","text":"Report","size":"501 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3083"}],"country":"Russia","otherGeospatial":"Bazhenov Formation of the West Siberian Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 69.12597656249999,\n 76.63922560965885\n ],\n [\n 68.73046875,\n 76.27912201826652\n ],\n [\n 67.4560546875,\n 76.04791619059357\n ],\n [\n 60.9521484375,\n 75.06168594936098\n ],\n [\n 60.16113281250001,\n 74.54332982677906\n ],\n [\n 56.953125,\n 73.12494524712693\n ],\n [\n 55.986328125,\n 72.10094360009953\n ],\n [\n 56.2939453125,\n 71.37110941823617\n ],\n [\n 58.75488281249999,\n 70.65633017009853\n ],\n [\n 60.29296874999999,\n 70.09552886456429\n ],\n [\n 63.8525390625,\n 69.71810669906763\n ],\n [\n 66.181640625,\n 69.1312712296365\n ],\n [\n 67.8955078125,\n 68.5443150407769\n ],\n [\n 67.3681640625,\n 67.7261081859507\n ],\n [\n 65.21484375,\n 67.47492238478702\n ],\n [\n 61.787109375,\n 66.60067571342496\n ],\n [\n 59.4140625,\n 65.44000165965534\n ],\n [\n 58.447265625,\n 64.14895190024562\n ],\n [\n 59.0185546875,\n 62.77483725887553\n ],\n [\n 59.45800781249999,\n 61.66902436927201\n ],\n [\n 63.7646484375,\n 58.424729753759124\n ],\n [\n 66.4892578125,\n 56.68037378950137\n ],\n [\n 67.32421875,\n 55.7765730186677\n ],\n [\n 69.521484375,\n 55.50374985927514\n ],\n [\n 71.4111328125,\n 55.229023057406344\n ],\n [\n 71.806640625,\n 54.41892996865827\n ],\n [\n 73.0810546875,\n 54.49556752187406\n ],\n [\n 74.6630859375,\n 54.265224078605684\n ],\n [\n 75.9814453125,\n 54.41892996865827\n ],\n [\n 79.2333984375,\n 54.6992335284814\n ],\n [\n 80.68359375,\n 55.27911529201561\n ],\n [\n 81.73828125,\n 56.63206372054474\n ],\n [\n 82.353515625,\n 58.00809779306888\n ],\n [\n 81.6943359375,\n 59.866883195210214\n ],\n [\n 81.7822265625,\n 62.02152819100765\n ],\n [\n 81.7822265625,\n 63.470144746565424\n ],\n [\n 81.4306640625,\n 65.80277639340238\n ],\n [\n 81.474609375,\n 67.7926408447319\n ],\n [\n 81.6064453125,\n 68.56038368664157\n ],\n [\n 81.73828125,\n 69.02141408835533\n ],\n [\n 79.62890625,\n 69.16255790810501\n ],\n [\n 78.3544921875,\n 69.88501003874241\n ],\n [\n 78.9697265625,\n 70.48089578887483\n ],\n [\n 79.8486328125,\n 70.94535555009823\n ],\n [\n 79.013671875,\n 71.51097803770108\n ],\n [\n 79.189453125,\n 71.78768067220726\n ],\n [\n 78.8818359375,\n 72.01972876525514\n ],\n [\n 78.0908203125,\n 72.36910450725075\n ],\n [\n 78.046875,\n 72.80308575588377\n ],\n [\n 75.4541015625,\n 73.20131708476698\n ],\n [\n 74.4873046875,\n 75.01630602063719\n ],\n [\n 74.5751953125,\n 75.90950400691688\n ],\n [\n 74.0478515625,\n 76.31035754301745\n ],\n [\n 73.037109375,\n 76.56795488916347\n ],\n [\n 70.9716796875,\n 76.68990582156862\n ],\n [\n 69.12597656249999,\n 76.63922560965885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
Coastal wetlands are among the most valuable ecosystems on Earth, where ecosystem services such as flood protection depend nonlinearly on wetland size and are threatened by sea level rise and coastal development. Here we propose a simple model of marsh migration into adjacent uplands and couple it with existing models of seaward edge erosion and vertical soil accretion to explore how ecosystem connectivity influences marsh size and response to sea level rise. We find that marsh loss is nearly inevitable where topographic and anthropogenic barriers limit migration. Where unconstrained by barriers, however, rates of marsh migration are much more sensitive to accelerated sea level rise than rates of edge erosion. This behavior suggests a counterintuitive, natural tendency for marsh expansion with sea level rise and emphasizes the disparity between coastal response to climate change with and without human intervention.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2016GL068507","usgsCitation":"Kirwan, M., Walters, D.C., Reay, W.G., and Carr, J., 2016, Sea level driven marsh expansion in a coupled model of marsh erosion and migration: Geophysical Research Letters, v. 43, no. 9, p. 4366-4373, https://doi.org/10.1002/2016GL068507.","productDescription":"8 p.","startPage":"4366","endPage":"4373","ipdsId":"IP-071126","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470428,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068507","text":"Publisher Index Page"},{"id":330911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-03","publicationStatus":"PW","scienceBaseUri":"58259560e4b01fad86db2412","contributors":{"authors":[{"text":"Kirwan, Matthew L. 0000-0002-0658-3038","orcid":"https://orcid.org/0000-0002-0658-3038","contributorId":84060,"corporation":false,"usgs":true,"family":"Kirwan","given":"Matthew L.","affiliations":[],"preferred":false,"id":653426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, David C.","contributorId":176766,"corporation":false,"usgs":false,"family":"Walters","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":653427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reay, William G.","contributorId":176767,"corporation":false,"usgs":false,"family":"Reay","given":"William","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":653428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653429,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178283,"text":"70178283 - 2016 - Climate change impacts on ecosystems and ecosystem services in the United States: Process and prospects for sustained assessment","interactions":[],"lastModifiedDate":"2020-07-28T15:29:24.14632","indexId":"70178283","displayToPublicDate":"2016-11-10T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate change impacts on ecosystems and ecosystem services in the United States: Process and prospects for sustained assessment","docAbstract":"The third United States National Climate Assessment emphasized an evaluation of not just the impacts of climate change on species and ecosystems, but also the impacts of climate change on the benefits that people derive from nature, known as ecosystem services. The ecosystems, biodiversity, and ecosystem services component of the assessment largely drew upon the findings of a transdisciplinary workshop aimed at developing technical input for the assessment, involving participants from diverse sectors. A small author team distilled and synthesized this and hundreds of other technical input to develop the key findings of the assessment. The process of developing and ranking key findings hinged on identifying impacts that had particular, demonstrable effects on the U.S. public via changes in national ecosystem services. Findings showed that ecosystem services are threatened by the impacts of climate change on water supplies, species distributions and phenology, as well as multiple assaults on ecosystem integrity that, when compounded by climate change, reduce the capacity of ecosystems to buffer against extreme events. As ecosystems change, such benefits as water sustainability and protection from storms that are afforded by intact ecosystems are projected to decline across the continent due to climate change. An ongoing, sustained assessment that focuses on the co-production of actionable climate science will allow scientists from a range of disciplines to ascertain the capability of their forecasting models to project environmental and ecological change and link it to ecosystem services; additionally, an iterative process of evaluation, development of management strategies, monitoring, and reevaluation will increase the applicability and usability of the science by the U.S. public.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-015-1547-3","usgsCitation":"Grimm, N.B., Groffman, P., Staudinger, M., and Tallis, H., 2016, Climate change impacts on ecosystems and ecosystem services in the United States: Process and prospects for sustained assessment: Climatic Change, v. 135, no. 1, p. 97-109, https://doi.org/10.1007/s10584-015-1547-3.","productDescription":"23 p.","startPage":"97","endPage":"109","ipdsId":"IP-061615","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":470427,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10.1007/s10584-015-1547-3","text":"External Repository"},{"id":330931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":207908,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle D.","affiliations":[{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":653507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tallis, Heather","contributorId":176800,"corporation":false,"usgs":false,"family":"Tallis","given":"Heather","email":"","affiliations":[],"preferred":false,"id":653517,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178361,"text":"70178361 - 2016 - Defining ecosystem assets for natural capital accounting","interactions":[],"lastModifiedDate":"2016-11-16T10:52:32","indexId":"70178361","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Defining ecosystem assets for natural capital accounting","docAbstract":"In natural capital accounting, ecosystems are assets that provide ecosystem services to people. Assets can be measured using both physical and monetary units. In the international System of Environmental-Economic Accounting, ecosystem assets are generally valued on the basis of the net present value of the expected flow of ecosystem services. In this paper we argue that several additional conceptualisations of ecosystem assets are needed to understand ecosystems as assets, in support of ecosystem assessments, ecosystem accounting and ecosystem management. In particular, we define ecosystems’ capacity and capability to supply ecosystem services, as well as the potential supply of ecosystem services. Capacity relates to sustainable use levels of multiple ecosystem services, capability involves prioritising the use of one ecosystem service over a basket of services, and potential supply considers the ability of ecosystems to generate services regardless of demand for these services. We ground our definitions in the ecosystem services and accounting literature, and illustrate and compare the concepts of flow, capacity, capability, and potential supply with a range of conceptual and real-world examples drawn from case studies in Europe and North America. Our paper contributes to the development of measurement frameworks for natural capital to support environmental accounting and other assessment frameworks.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0164460","usgsCitation":"Hein, L., Bagstad, K.J., Edens, B., Obst, C., de Jong, R., and Lesschen, J.P., 2016, Defining ecosystem assets for natural capital accounting: PLoS ONE, v. 11, no. 11, e0164460; 25 p., https://doi.org/10.1371/journal.pone.0164460.","productDescription":"e0164460; 25 p.","ipdsId":"IP-075433","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164460","text":"Publisher Index Page"},{"id":331071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582dd8eae4b04d580bd3fa91","contributors":{"authors":[{"text":"Hein, Lars","contributorId":176849,"corporation":false,"usgs":false,"family":"Hein","given":"Lars","email":"","affiliations":[],"preferred":false,"id":653773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":653772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edens, Bram","contributorId":176850,"corporation":false,"usgs":false,"family":"Edens","given":"Bram","email":"","affiliations":[],"preferred":false,"id":653774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Obst, Carl","contributorId":176851,"corporation":false,"usgs":false,"family":"Obst","given":"Carl","email":"","affiliations":[],"preferred":false,"id":653775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Jong, Rixt","contributorId":176852,"corporation":false,"usgs":false,"family":"de Jong","given":"Rixt","email":"","affiliations":[],"preferred":false,"id":653776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lesschen, Jan Peter","contributorId":176853,"corporation":false,"usgs":false,"family":"Lesschen","given":"Jan","email":"","middleInitial":"Peter","affiliations":[],"preferred":false,"id":653777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178093,"text":"ofr20161187 - 2016 - Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","interactions":[],"lastModifiedDate":"2018-03-08T16:08:01","indexId":"ofr20161187","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1187","title":"Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","docAbstract":"Understanding if and how community exposure to coastal hazards may change over time is crucial information for coastal managers tasked with developing climate adaptation plans. This report summarizes estimates of population and asset exposure to coastal-inundation hazards associated with sea-level-rise and storm scenarios in six coastal communities of the Great Marsh region of Essex County, Massachusetts. This U.S. Geological Survey (USGS) analysis was conducted in collaboration with National Wildlife Federation (NWF) representatives, who are working with local stakeholders to develop local climate adaptation plans for the Towns of Salisbury, Newbury, Rowley, Ipswich, and Essex and the City of Newburyport (hereafter referred to as communities). Community exposure was characterized by integrating various community indicators (land cover and land use, population, economic assets, critical facilities, and infrastructure) with coastal-hazard zones that estimate inundation extents and water depth for three time periods.
Estimates of community exposure are based on the presence of people, businesses, and assets in hazard zones that are calculated from geospatial datasets using geographic-information-system (GIS) tools. Results are based on current distributions of people and assets in hazard zones and do not take into account projections of human population, asset, or land-use changes over time. Results are not loss estimates based on engineering analysis or field surveys for any particular facility and do not take into account aspects of individual and household preparedness before an extreme event, adaptive capacity of a community during an event, or long-term resilience of individuals and communities after an event. Potential losses would match reported inventories only if all residents, business owners, public managers, and elected officials were unaware of what to do if warned of an imminent threat, failed to take protective measures during an extreme event, or failed to implement any long-term strategies to mitigate potential impacts. This analysis is intended to serve as a foundation for additional risk-related studies, plans, and mitigation efforts that are tailored to local needs. After a summary of the geospatial methods used in the analysis, results are organized by community so that local officials can easily use them in their local adaptation planning efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161187","usgsCitation":"Abdollahian, Nina, Jones, J.L., and Wood, N.J., 2016, Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts: U.S. Geological Survey Open-File Report 2016–1187, 87 p., https://dx.doi.org/10.3133/ofr20161187.","productDescription":"ix, 97 p.","onlineOnly":"Y","ipdsId":"IP-076664","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":330895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1187/ofr20161187.pdf","text":"Report","size":"37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1187"},{"id":330894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1187/coverthb.jpg"}],"country":"United States","state":"Massachusetts","county":"Essex County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.71006774902344,\n 42.68193062780271\n ],\n [\n -70.68603515625,\n 42.661736441708726\n ],\n [\n -70.67985534667969,\n 42.645576368740564\n ],\n [\n -70.68946838378906,\n 42.61374895431491\n ],\n [\n -70.67779541015624,\n 42.58594981115061\n ],\n [\n -70.70938110351562,\n 42.56926437219384\n ],\n [\n -70.74783325195311,\n 42.5626900946744\n ],\n [\n -70.79315185546875,\n 42.55813826591757\n ],\n [\n -70.81443786621094,\n 42.5530802889558\n ],\n [\n -70.84396362304688,\n 42.549033612225145\n ],\n [\n -70.90164184570312,\n 42.558644041066486\n ],\n [\n -70.92567443847656,\n 42.58443313755392\n ],\n [\n -70.94352722167969,\n 42.61273829368574\n ],\n [\n -70.9771728515625,\n 42.65315192577889\n ],\n [\n -70.98609924316406,\n 42.6970719613491\n ],\n [\n -71.00807189941406,\n 42.74196953252084\n ],\n [\n -71.01837158203125,\n 42.77221922375653\n ],\n [\n -71.026611328125,\n 42.818573401893836\n ],\n [\n -71.03347778320312,\n 42.85834982271733\n ],\n [\n -70.99296569824219,\n 42.86740922288482\n ],\n [\n -70.94627380371094,\n 42.877473663905946\n ],\n [\n -70.92498779296875,\n 42.882505268943774\n ],\n [\n -70.88447570800781,\n 42.882505268943774\n ],\n [\n -70.85975646972656,\n 42.87495770751784\n ],\n [\n -70.83915710449219,\n 42.87042872752173\n ],\n [\n -70.81718444824219,\n 42.87042872752173\n ],\n [\n -70.806884765625,\n 42.835191982798925\n ],\n [\n -70.8013916015625,\n 42.79288135293722\n ],\n [\n -70.77392578125,\n 42.73944805837745\n ],\n [\n -70.73204040527342,\n 42.70110902692345\n ],\n [\n -70.71006774902344,\n 42.68193062780271\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Western Geographic Science Center
U.S. Geological Survey
345 Middlefield Road, MS 531
Menlo Park, CA 94025
http://geography.wr.usgs.gov/
During September–November 2012, the U.S. Geological Survey, in cooperation with the Puerto Rico Aqueduct and Sewer Authority, conducted a sedimentation survey of Lago Caonillas to estimate current (2012) reservoir storage capacity and the recent (2000–2012) reservoir sedimentation rate by comparing the 2012 bathymetric survey data with the February 2000 data. The Lago Caonillas storage capacity, which was 42.27 million cubic meters in February 2000, decreased to 39.55 million cubic meters by September–November 2012. The intersurvey (2000–2012) storage capacity loss was about 6 percent, corresponding to a decrease of about 0.5 percent per year; this loss represents a reservoir sedimentation rate of about 226,670 cubic meters per year between 2000 and 2012. On a long-term basis, however, the sedimentation rate has remained nearly constant, decreasing from about 257,500 to 251,720 cubic meters per year during 1948–2000 and 1948–2012, respectively. Most of the sediment accumulation and associated storage capacity loss of Lago Caonillas has occurred within the eastern and Río Caonillas branches of the reservoir. In the vicinity of the Caonillas Dam, minor sediment deposition and scour have occurred. The Lago Caonillas drainage area sediment yield has decreased by about 2 percent since the previous survey, from 1,266 cubic meters per square kilometer per year in 2000 to 1,237 cubic meters per square kilometer per year in 2012. If the long-term sedimentation rate of 251,720 cubic meters per year remains constant, the useful life of Lago Caonillas may end in about 2169.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3368","collaboration":"Prepared in cooperation with the Puerto Rico Aqueduct and Sewer Authority","usgsCitation":"Soler-López, L.R., 2016, Sedimentation survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012: U.S. Geological Survey Scientific Investigations Map 3368, 1 sheet, https://dx.doi.org/10.3133/sim3368.","productDescription":"29.00 x 30.83 inches","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-055426","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438510,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74M92NT","text":"USGS data release","linkHelpText":"Spatial Data for Sedimentation Survey of Lago Caonillas, Utuado, Puerto Rico, SeptemberNovember 2012"},{"id":330594,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3368/sim3368.pdf","text":"Sheet 1","size":"1.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3368"},{"id":330593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3368/coverthb.jpg"},{"id":330666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F74M92NT","text":"USGS data release - Spatial Data for Sedimentation Survey of Lago Caonillas, Utuado, Puerto Rico, September–November 2012"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.8,\n 18.2\n ],\n [\n -66.8,\n 18.5\n ],\n [\n -66.5,\n 18.5\n ],\n [\n -66.5,\n 18.2\n ],\n [\n -66.8,\n 18.2\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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Lutz, FL 33559
This map shows the generalized geology of Alaska, which helps us to understand where potential mineral deposits and energy resources might be found, define ecosystems, and ultimately, teach us about the earth history of the State. Rock units are grouped in very broad categories on the basis of age and general rock type. A much more detailed and fully referenced presentation of the geology of Alaska is available in the Geologic Map of Alaska (http://dx.doi.org/10.3133/sim3340). This product represents the simplification of thousands of individual rock units into just 39 broad groups. Even with this generalization, the sheer complexity of Alaskan geology remains evident.
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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean undiscovered, technically recoverable continuous resources that total 464 million barrels of oil and 4.08 trillion cubic feet of gas in the Lower Mississippian Sunbury Shale and Middle Devonian–Lower Mississippian Chattanooga Shale of the Appalachian Basin Province.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163085","usgsCitation":"Higley, D.K., Rouse, W.A., Enomoto, C.B., Trippi, M.H., Klett, T.R., Mercier, T.J., Brownfield, M.E., Tennyson, M.E., Drake, R.M., II, Finn, T.M., Gianoutsos, N.J., Pearson, O.N., Doolan, Colin, Le, P.A., and Schenk, C.J., Assessment of undiscovered oil and gas resources of the Mississippian Sunbury shale and Devonian–Mississippian Chattanooga shale in the Appalachian Basin Province, 2016: U.S. Geological Survey Fact Sheet 2016–3085, 2 p., https://dx.doi.org/10.3133/fs20163085.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-076996","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":438511,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLVVQ2","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Sunbury and Chattanooga Shales Assessment Unit Boundaries and Assessment Input Forms"},{"id":330779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3085/fs20163085.pdf","text":"Report","size":"382 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3085"},{"id":330778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3085/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.705078125,\n 41.44272637767212\n ],\n [\n -83.3642578125,\n 38.634036452919226\n ],\n [\n -86.7919921875,\n 34.994003757575776\n ],\n [\n -87.60498046875,\n 32.36140331527543\n ],\n [\n -87.01171875,\n 32.0639555946604\n ],\n [\n -85.45166015624999,\n 33.94335994657882\n ],\n [\n -84.22119140625,\n 35.38904996691167\n ],\n [\n -81.5185546875,\n 36.59788913307022\n ],\n [\n -79.27734374999999,\n 37.70120736474139\n ],\n [\n -79.12353515625,\n 42.68243539838623\n ],\n [\n -82.705078125,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
http://energy.usgs.gov
Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
","language":"English","publisher":"JoVE","doi":"10.3791/54578","usgsCitation":"West, A.M., Evangelista, P.H., Jarnevich, C.S., Young, N.E., Stohlgren, T.J., Talbert, C., Talbert, M., Morisette, J., and Anderson, R., 2016, Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM): Journal of Visualized Experiments, v. 116, e54578, https://doi.org/10.3791/54578.","productDescription":"e54578","ipdsId":"IP-070978","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5092193","text":"Publisher Index Page"},{"id":330808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"5821a0dbe4b02f1a881de95a","contributors":{"authors":[{"text":"West, Amanda M.","contributorId":176705,"corporation":false,"usgs":false,"family":"West","given":"Amanda","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evangelista, Paul H.","contributorId":14747,"corporation":false,"usgs":true,"family":"Evangelista","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":653201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbert, Colin talbertc@usgs.gov","contributorId":4668,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":653205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Talbert, Marian mtalbert@usgs.gov","contributorId":5180,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":false,"id":653206,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morisette, Jeffrey","contributorId":100739,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","affiliations":[],"preferred":false,"id":653207,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, Ryan","contributorId":106029,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","affiliations":[],"preferred":false,"id":653208,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70168511,"text":"sir20155142 - 2016 - Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010","interactions":[],"lastModifiedDate":"2021-09-27T18:32:45.902579","indexId":"sir20155142","displayToPublicDate":"2016-11-07T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5142","title":"Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010","docAbstract":"The Delaware River Basin (DRB) was selected as a Focus Area Study in 2011 by the U.S. Geological Survey (USGS) as part of the USGS National Water Census. The National Water Census is a USGS research program that focuses on national water availability and use and then develops new water accounting tools and assesses water availability at both the regional and national scales. One of the water management needs that the DRB study addressed, and that was identified by stakeholder groups from the DRB, was to improve the integration of state water use and water-supply data and to provide the compiled water use information to basin users. This water use information was also used in the hydrologic modeling and ecological components of the study.
Instream and offstream water use was calculated for 2010 for the DRB based on information received from Delaware, New Jersey, New York, and Pennsylvania. Water withdrawal, interbasin transfers, return flow, and hydroelectric power generation release data were compiled for 11 categories by hydrologic subregion, basin, subbasin, and subwatershed. Data availability varied by state. Site-specific data were used whenever possible to calculate public supply, irrigation (golf courses, nurseries, sod farms, and crops), aquaculture, self-supplied industrial, commercial, mining, thermoelectric, and hydroelectric power withdrawals. Where site-specific data were not available, primarily for crop irrigation, livestock, and domestic use, various techniques were used to estimate water withdrawals.
Total water withdrawals in the Delaware River Basin were calculated to be about 7,130 million gallons per day (Mgal/d) in 2010. Calculations of withdrawals by source indicate that freshwater withdrawals were about 4,130 Mgal/d (58 percent of the total) and the remaining 3,000 Mgal/d (42 percent) were from saline water. Total surface-water withdrawals were calculated to be 6,590 Mgal/d, or 92 percent of the total; about 54 percent (3,590 Mgal/d) of surface water withdrawn was freshwater. Total groundwater withdrawals were calculated to be 545 Mgal/d (8 percent of the total), all of which was freshwater. During 2010, calculated withdrawals by category, in decreasing order, were: thermoelectric power, 4,910 Mgal/d; public supply, 1,490 Mgal/d; self-supplied industrial, 350 Mgal/d; irrigation, 175 Mgal/d; self-supplied domestic, 117 Mgal/d; mining, 41.3 Mgal/d; aquaculture, 19.3 Mgal/d; livestock, 6.72 Mgal/d, and commercial, 5.89 Mgal/d. The amount of instream use for hydroelectric power generation purposes in 2010 was reported to be 273 Mgal/d for the Wallenpaupack Plant and 127 Mgal/d for the Mongaup River system.
Total return flows in the DRB were 2,960 Mgal/d in 2010. Although municipal wastewater-treatment plants accounted for 539 (97 percent) of the return-flow sites, they accounted for about 70 percent of the total return flows in the DRB. There was limited information on return flows from thermoelectric power.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155142","usgsCitation":"Hutson, S.S., Linsey, K.S., Ludlow, R.A., Reyes, Betzaida, and Shourds, J.L., 2016, Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010: U.S. Geological Survey Scientific Investigations Report 2015–5142, 76 p., https://dx.doi.org/10.3133/sir20155142.","productDescription":"Report: vii, 76 p.; 2 Appendixes; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058986","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":438513,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM787C","text":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8 and HUC12) in the Delaware River Basin, 2010"},{"id":330727,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix3.xlsx","text":"Appendix 3","size":"269 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5142 Appendix 3","linkHelpText":"- Delaware River Basin Water Use by Subbasin, 2010"},{"id":330724,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5142/coverthb.jpg"},{"id":330725,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142.pdf","text":"Report","size":"57.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5142"},{"id":330726,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix2.docx","text":"Appendix 2","size":"2.49 MB docx","description":"SIR 2015-5142 Appendix 2","linkHelpText":"- Hydrologic Subbasins, Watersheds, and Subwatersheds in the Delaware River Basin"},{"id":330728,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TM787C","text":"USGS data release","description":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8) and Subwatershed (HUC12) in the Delaware River Basin, 2010"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.652099609375,\n 39.18117526158749\n ],\n [\n -74.50927734375,\n 40.195659093364654\n ],\n [\n -74.586181640625,\n 41.36856413680967\n ],\n [\n -74.300537109375,\n 42.09822241118974\n ],\n [\n -74.619140625,\n 42.53689200787315\n ],\n [\n -75.498046875,\n 42.10637370579324\n ],\n [\n -75.9375,\n 41.12074559016745\n ],\n [\n -76.607666015625,\n 40.36328834091583\n ],\n [\n -75.860595703125,\n 39.715638134796336\n ],\n [\n -75.43212890625,\n 38.685509760012\n ],\n [\n -75.069580078125,\n 38.77121637244273\n ],\n [\n -74.652099609375,\n 39.18117526158749\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator—National Water Census
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Or visit the National Water Census Web site at: http://water.usgs.gov/watercensus
","tableOfContents":"Infrastructure—the physical framework of transportation, energy, communications, water supply, and other systems—and construction management—the overall planning, coordination, and control of a project from beginning to end—are critical to the Nation’s prosperity. The American Society of Civil Engineers has warned that, despite the importance of the Nation’s infrastructure, it is in fair to poor condition and needs sizable and urgent investments to maintain and modernize it, and to ensure that it is sustainable and resilient.
Three-dimensional (3D) light detection and ranging (lidar) elevation data provide valuable productivity, safety, and cost-saving benefits to infrastructure improvement projects and associated construction management. By providing data to users, the 3D Elevation Program (3DEP) of the U.S. Geological Survey reduces users’ costs and risks and allows them to concentrate on their mission objectives. 3DEP includes (1) data acquisition partnerships that leverage funding, (2) contracts with experienced private mapping firms, (3) technical expertise, lidar data standards, and specifications, and (4) most important, public access to high-quality 3D elevation data.
The size and breadth of improvements for the Nation’s infrastructure and construction management needs call for an efficient, systematic approach to acquiring foundational 3D elevation data. The 3DEP approach to national data coverage will yield large cost savings over individual project-by-project acquisitions and will ensure that data are accessible for other critical applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163093","usgsCitation":"Lukas, Vicki, and Carswell, W.J., Jr., 2016, The 3D Elevation Program and America's infrastructure: U.S. Geological Survey Fact Sheet 2016–3093, 2 p., https://dx.doi.org/10.3133/fs20163093.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077294","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":330670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3093/fs20163093.pdf","text":"Report","size":"425 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3093"},{"id":330669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3093/coverthb.jpg"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
511 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
Decision support tools can aid decision making by systematically incorporating information, accounting for uncertainties, and facilitating evaluation between alternatives. Without user buy-in, however, decision support tools can fail to influence decision-making processes. We surveyed fishery researchers, managers, and fishers affiliated with the Lake Whitefish Coregonus clupeaformis fishery in the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior to assess opinions of current and future management needs to identify barriers to, and opportunities for, developing a decision support tool based on Lake Whitefish recruitment projections with climate change. Approximately 64% of 39 respondents were satisfied with current management, and nearly 85% agreed that science was well integrated into management programs. Though decision support tools can facilitate science integration into management, respondents suggest that they face significant implementation barriers, including lack of political will to change management and perceived uncertainty in decision support outputs. Recommendations from this survey can inform development of decision support tools for fishery management in the Great Lakes and other regions.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/03632415.2016.1232960","usgsCitation":"Lynch, A.J., Taylor, W., and McCright, A.M., 2016, Stakeholder views of management and decision support tools to integrate climate change into Great Lakes Lake Whitefish management: Fisheries, v. 41, no. 11, p. 644-652, https://doi.org/10.1080/03632415.2016.1232960.","productDescription":"9 p.","startPage":"644","endPage":"652","ipdsId":"IP-067035","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":330860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"5821a0dce4b02f1a881de96d","contributors":{"authors":[{"text":"Lynch, Abigail J. 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":5645,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":false,"id":653320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, William W.","contributorId":113795,"corporation":false,"usgs":true,"family":"Taylor","given":"William W.","affiliations":[],"preferred":false,"id":653321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCright, Aaron M.","contributorId":176733,"corporation":false,"usgs":false,"family":"McCright","given":"Aaron","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}