Mobile species with complex spatial dynamics can be difficult to manage because their population distributions vary across space and time, and because the consequences of managing particular habitats are uncertain when evaluated at the level of the entire population. Metrics to assess the importance of habitats and pathways connecting habitats in a network are necessary to guide a variety of management decisions. Given the many metrics developed for spatially structured models, it can be challenging to select the most appropriate one for a particular decision. To guide the management of spatially structured populations, we define three classes of metrics describing habitat and pathway quality based on their data requirements (graph-based, occupancy-based, and demographic-based metrics) and synopsize the ecological literature relating to these classes. Applying the first steps of a formal decision-making approach (problem framing, objectives, and management actions), we assess the utility of metrics for particular types of management decisions. Our framework can help managers with problem framing, choosing metrics of habitat and pathway quality, and to elucidate the data needs for a particular metric. Our goal is to help managers to narrow the range of suitable metrics for a management project, and aid in decision-making to make the best use of limited resources.
","language":"English","publisher":"Elsevier","usgsCitation":"Nicol, S., Wiederholt, R., Diffendorfer, J., Mattsson, B., Thogmartin, W.E., Semmens, D.J., Laura Lopez-Hoffman, and Norris, R., 2016, A management-oriented framework for selecting metrics used to assess habitat- and path-specific quality in spatially structured populations: Ecological Indicators, v. 69, p. 792-802.","productDescription":"10 p.","startPage":"792","endPage":"802","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071948","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":323371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":323299,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S1470160X1630262X"}],"volume":"69","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"575a932ee4b04f417c275118","contributors":{"authors":[{"text":"Nicol, Sam","contributorId":171610,"corporation":false,"usgs":false,"family":"Nicol","given":"Sam","email":"","affiliations":[{"id":26927,"text":"CSIRO, Australia","active":true,"usgs":false}],"preferred":false,"id":638088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiederholt, Ruscena","contributorId":171611,"corporation":false,"usgs":false,"family":"Wiederholt","given":"Ruscena","email":"","affiliations":[{"id":12738,"text":"U of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":638089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":638087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mattsson, Brady J.","contributorId":171612,"corporation":false,"usgs":false,"family":"Mattsson","given":"Brady J.","affiliations":[{"id":26928,"text":"Univ. of Vienna","active":true,"usgs":false}],"preferred":false,"id":638090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":638091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":638092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Laura Lopez-Hoffman","contributorId":171613,"corporation":false,"usgs":false,"family":"Laura Lopez-Hoffman","affiliations":[{"id":12738,"text":"U of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":638093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Norris, Ryan","contributorId":171614,"corporation":false,"usgs":false,"family":"Norris","given":"Ryan","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":638094,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70173658,"text":"70173658 - 2016 - Model selection and assessment for multi-species occupancy models","interactions":[],"lastModifiedDate":"2016-07-01T12:45:21","indexId":"70173658","displayToPublicDate":"2016-06-08T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Model selection and assessment for multi-species occupancy models","docAbstract":"While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-1471.1","usgsCitation":"Broms, K.M., Hooten, M., and Fitzpatrick, R.M., 2016, Model selection and assessment for multi-species occupancy models: Ecology, v. 97, no. 7, p. 1759-1770, https://doi.org/10.1890/15-1471.1.","productDescription":"12 p.","startPage":"1759","endPage":"1770","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068638","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"575933b1e4b04f417c253d14","contributors":{"authors":[{"text":"Broms, Kristin M.","contributorId":171524,"corporation":false,"usgs":false,"family":"Broms","given":"Kristin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":637464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Ryan M.","contributorId":55746,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Ryan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637847,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170922,"text":"ds999 - 2016 - Sedimentologic characteristics of recent washover deposits from Assateague Island, Maryland","interactions":[],"lastModifiedDate":"2025-05-13T16:49:55.913774","indexId":"ds999","displayToPublicDate":"2016-06-08T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"999","title":"Sedimentologic characteristics of recent washover deposits from Assateague Island, Maryland","docAbstract":"The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. Although physical changes caused by tropical and extratropical storms to the sandy beaches and dunes fronting barrier islands are generally well documented, the interaction between sandy shoreline erosion and overwash with the back-barrier wetland and estuarine environments is poorly constrained. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, causing erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments.
\nThis report describes sediment data collected using sand augers in active overwash zones on Assateague Island in Maryland. Samples were collected by the U.S. Geological Survey (USGS) during two surveys in March/April and October 2014 (USGS Field Activity Numbers [FAN] 2014-301-FA and 2014-322-FA, respectively). The physical characteristics (for example, sediment texture or bedding structure) of and spatial differences among these deposits will provide information about overwash processes and sediment transport from the sandy barrier-island reaches to the back-barrier environments. Metrics derived from these data, such as mean grain size or deposit thicknesses, can be used to ground-truth remote sensing and geophysical data and can also be incorporated into sediment transport models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, the results of sediment grain-size analyses, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds999","usgsCitation":"Bernier, J.C., Zaremba, N.J., Wheaton, C.J., Ellis, A.M., Marot, M.E., and Smith, C.G., 2016, Sedimentologic characteristics of recent washover deposits from Assateague Island, Maryland: U.S. Geological Survey Data Series 999, https://dx.doi.org/10.3133/ds999.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070719","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":321606,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0999"},{"id":323239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":399133,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_104286.htm"}],"country":"United States","state":"Maryland","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.09841918945311,\n 38.32765244536364\n ],\n [\n -75.08880615234375,\n 38.32388174326885\n ],\n [\n -75.09361267089844,\n 38.313645991657935\n ],\n [\n -75.157470703125,\n 38.16749461578964\n ],\n [\n -75.21377563476562,\n 38.05295746694274\n ],\n [\n -75.28999328613281,\n 37.947446401992934\n ],\n [\n -75.32981872558594,\n 37.88081521949766\n ],\n [\n -75.34629821777344,\n 37.86292829402713\n ],\n [\n -75.36552429199219,\n 37.849916893514944\n ],\n [\n -75.39779663085938,\n 37.84612146910074\n ],\n [\n -75.40054321289062,\n 37.85804928797244\n ],\n [\n -75.4046630859375,\n 37.87647939392142\n ],\n [\n -75.39299011230469,\n 37.88623464254098\n ],\n [\n -75.37994384765625,\n 37.90032327590217\n ],\n [\n -75.38063049316406,\n 37.91278405007035\n ],\n [\n -75.36346435546874,\n 37.917117737741826\n ],\n [\n -75.35041809082031,\n 37.92090950501414\n ],\n [\n -75.33531188964844,\n 37.929575667562325\n ],\n [\n -75.3277587890625,\n 37.93986540897977\n ],\n [\n -75.32295227050781,\n 37.94690492842903\n ],\n [\n -75.32295227050781,\n 37.962064679782394\n ],\n [\n -75.31539916992188,\n 37.977762552327654\n ],\n [\n -75.30715942382812,\n 37.996162679728116\n ],\n [\n -75.29205322265625,\n 38.01455819225335\n ],\n [\n -75.27626037597656,\n 38.034030762875844\n ],\n [\n -75.27008056640625,\n 38.059986139487975\n ],\n [\n -75.25016784667969,\n 38.08052761936274\n ],\n [\n -75.24055480957031,\n 38.10322464859825\n ],\n [\n -75.23368835449219,\n 38.11349003681576\n ],\n [\n -75.2178955078125,\n 38.14103736644331\n ],\n [\n -75.201416015625,\n 38.17559185481662\n ],\n [\n -75.18150329589844,\n 38.20797181420939\n ],\n [\n -75.17189025878906,\n 38.22091976683121\n ],\n [\n -75.15678405761719,\n 38.238180119798635\n ],\n [\n -75.14442443847656,\n 38.250583468289555\n ],\n [\n -75.13687133789061,\n 38.26891497618184\n ],\n [\n -75.12931823730467,\n 38.28346905497185\n ],\n [\n -75.12176513671874,\n 38.29586467286608\n ],\n [\n -75.11283874511719,\n 38.31041334882078\n ],\n [\n -75.09841918945311,\n 38.32765244536364\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
The main goal of this study was to gain knowledge on post-release survival and movement of Western Grebes (Aechmophorus occidentalis) using a modified technique for implanting satellite transmitters. This technique had improved post-surgical survival in an earlier study. Nine Western Grebes, implanted with intracoelomic (within the body cavity) satellite transmitters with percutaneous antennae, were released close to their capture site in San Francisco Bay, California, USA. Eight survived at least 25 days (average number of transmittal days was 140.8), while two had transmitters that provided data for greater than 1 year (436 and 454 days). The average cumulative distance recorded for all Western Grebes (n = 9) was 829 km with two round-trip movements documented. One individual Western Grebe traveled a cumulative round-trip distance of 2,144 km in July and November 2011, while another individual traveled a round-trip distance of 1,514 km between 8 and 14 December 2011. This study provides a step forward in testing implantable satellite transmitters in Western Grebes and highlights the need to further improve tracking methods, potentially improving our understanding of their population threats.
","language":"English","publisher":"Waterbird Society","doi":"10.1675/063.039.0208","usgsCitation":"Mills, K.L., Gaydos, J.K., Fiorello, C.V., Whitmer, E., De La Cruz, S., Mulcahy, D.M., Vilchis, L.I., and Ziccardi, M.H., 2016, Post-release survival and movement of Western Grebes (Aechmophorus occidentalis) implanted with intracoelomic satellite transmitters: Waterbirds, v. 39, no. 2, p. 175-186, https://doi.org/10.1675/063.039.0208.","productDescription":"12 p.","startPage":"175","endPage":"186","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051534","costCenters":[{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":324763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake, Drews Reservoir, San Francisco Bay, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.07958984375001,\n 32.602361666817515\n ],\n [\n -124.07958984375001,\n 43.24520272203359\n ],\n [\n -116.3232421875,\n 43.24520272203359\n ],\n [\n -116.3232421875,\n 32.602361666817515\n ],\n [\n -124.07958984375001,\n 32.602361666817515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"577e2bb1e4b0ef4d2f445a36","contributors":{"authors":[{"text":"Mills, Kyra L.","contributorId":172677,"corporation":false,"usgs":false,"family":"Mills","given":"Kyra","email":"","middleInitial":"L.","affiliations":[{"id":27076,"text":"Oiled Wildlife Care Network, UC Davis","active":true,"usgs":false}],"preferred":false,"id":641545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaydos, Joseph K.","contributorId":28456,"corporation":false,"usgs":true,"family":"Gaydos","given":"Joseph","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":641546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fiorello, Christine V.","contributorId":172678,"corporation":false,"usgs":false,"family":"Fiorello","given":"Christine","email":"","middleInitial":"V.","affiliations":[{"id":27076,"text":"Oiled Wildlife Care Network, UC Davis","active":true,"usgs":false}],"preferred":false,"id":641547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitmer, Emily","contributorId":172679,"corporation":false,"usgs":false,"family":"Whitmer","given":"Emily","email":"","affiliations":[{"id":27076,"text":"Oiled Wildlife Care Network, UC Davis","active":true,"usgs":false}],"preferred":false,"id":641548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De La Cruz, Susan sdelacruz@usgs.gov","contributorId":131159,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"sdelacruz@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":641549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mulcahy, Daniel M. dmulcahy@usgs.gov","contributorId":3102,"corporation":false,"usgs":true,"family":"Mulcahy","given":"Daniel","email":"dmulcahy@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":641550,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vilchis, L. Ignacio","contributorId":172680,"corporation":false,"usgs":false,"family":"Vilchis","given":"L.","email":"","middleInitial":"Ignacio","affiliations":[{"id":24831,"text":"San Diego Zoo Institute for Conservation Research","active":true,"usgs":false}],"preferred":false,"id":641551,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ziccardi, Michael H.","contributorId":16677,"corporation":false,"usgs":true,"family":"Ziccardi","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":641552,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70173674,"text":"70173674 - 2016 - Long-term lesser prairie-chicken nest ecology in response to grassland management","interactions":[],"lastModifiedDate":"2016-06-07T15:20:37","indexId":"70173674","displayToPublicDate":"2016-06-07T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Long-term lesser prairie-chicken nest ecology in response to grassland management","docAbstract":"Long-term population and range declines from habitat loss and fragmentation caused the lesser prairie-chicken (Tympanuchus pallidicinctus) to be a species of concern throughout its range. Current lesser prairie-chicken range in New Mexico and Texas is partially restricted to sand shinnery oak (Quercus havardii; hereafter shinnery oak) prairies, on which cattle grazing is the main socioeconomic driver for private landowners. Cattle producers within shinnery oak prairies often focus land management on shrub eradication using the herbicide tebuthiuron to promote grass production for forage; however, herbicide application alone, and in combination with grazing, may affect nest site selection and nest survival of lesser prairie-chickens through the reduction of shinnery oak and native grasses. We used a controlled, paired, completely randomized design study to assess the influence of grazing and tebuthiuron application and their combined use on nest site selection and nest survival from 2001 to 2010 in Roosevelt County, New Mexico, USA at 2 spatial scales (i.e., treatment and microhabitat) in 4 treatments: tebuthiuron with grazing, tebuthiuron without grazing, no tebuthiuron with grazing, and a control of no tebuthiuron and no grazing. Grazing treatment was a short-duration system in which plots were grazed once during the dormant season and once during the growing season. Stocking rate was calculated each season based on measured forage production and applied to remove ≤25% of available herbaceous material per season. At the treatment scale, we compared nest site selection among treatments using 1-way χ2 tests and nest survival among treatments using a priori candidate nest survival models in Program MARK. At the microhabitat scale, we identified important habitat predictors of nest site selection and nest survival using logistic regression and a priori candidate nest survival models in Program MARK, respectively. Females typically used treatments as expected and we did not detect trends in selection. Nest survival did not differ among treatments. At the microhabitat scale, nest sites had less bare ground (P = 0.001) and greater angles of obstruction (P ≤ 0.001) compared to random sites. There was a high degree of model selection uncertainty among our candidate models at the microhabitat scale and survival estimates were similar among habitat covariates. Results suggest a tebuthiuron application rate of 0.60 kg/ha, short-duration grazing, and a combination of these management techniques were not detrimental to lesser prairie-chicken nest site selection or nest survival. However, intensified management that increases bare ground or reduces overhead cover may negatively affect lesser prairie-chicken nesting habitat and nest survival.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.1042","usgsCitation":"Fritts, S., Grisham, B.A., Haukos, D.A., Boal, C.W., Patten, M., Wolfe, D.H., Dixon, C., Cox, R.D., and Heck, W.R., 2016, Long-term lesser prairie-chicken nest ecology in response to grassland management: Journal of Wildlife Management, v. 80, no. 3, p. 527-539, https://doi.org/10.1002/jwmg.1042.","productDescription":"13 p.","startPage":"527","endPage":"539","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068763","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":323198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-13","publicationStatus":"PW","scienceBaseUri":"5757e21fe4b04f417c2426a3","contributors":{"authors":[{"text":"Fritts, Sarah R.","contributorId":171485,"corporation":false,"usgs":false,"family":"Fritts","given":"Sarah R.","affiliations":[],"preferred":false,"id":637598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grisham, Blake A.","contributorId":75419,"corporation":false,"usgs":true,"family":"Grisham","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":637599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":637482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":637600,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patten, Michael","contributorId":169287,"corporation":false,"usgs":false,"family":"Patten","given":"Michael","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":637601,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolfe, Don H.","contributorId":99478,"corporation":false,"usgs":true,"family":"Wolfe","given":"Don","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":637602,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dixon, Charles","contributorId":68203,"corporation":false,"usgs":true,"family":"Dixon","given":"Charles","email":"","affiliations":[],"preferred":false,"id":637603,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cox, Robert D.","contributorId":26240,"corporation":false,"usgs":true,"family":"Cox","given":"Robert","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":637604,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Heck, Willard R.","contributorId":61732,"corporation":false,"usgs":true,"family":"Heck","given":"Willard","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":637605,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70173696,"text":"70173696 - 2016 - Management decision making for fisher populations informed by occupancy modeling","interactions":[],"lastModifiedDate":"2016-06-16T11:30:57","indexId":"70173696","displayToPublicDate":"2016-06-07T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Management decision making for fisher populations informed by occupancy modeling","docAbstract":"Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km2 region of southern New York in areas that were currently open to fisher (Pekania [Martes] pennanti) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km2) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management decision making when harvest-related data are unavailable and when budgetary constraints do not allow for capture–recapture studies to directly estimate density.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21077","usgsCitation":"Fuller, A.K., Linden, D.W., and Royle, J., 2016, Management decision making for fisher populations informed by occupancy modeling: Journal of Wildlife Management, v. 80, no. 5, p. 794-802, https://doi.org/10.1002/jwmg.21077.","productDescription":"9 p.","startPage":"794","endPage":"802","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067072","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-27","publicationStatus":"PW","scienceBaseUri":"5757e21fe4b04f417c2426a7","contributors":{"authors":[{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":637517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Linden, Daniel W.","contributorId":171466,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":637546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":138865,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":637518,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173697,"text":"70173697 - 2016 - Understanding landowner intentions to create early successional forest habitat in the northeastern United States","interactions":[],"lastModifiedDate":"2016-06-07T13:18:56","indexId":"70173697","displayToPublicDate":"2016-06-07T14:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Understanding landowner intentions to create early successional forest habitat in the northeastern United States","docAbstract":"Early successional forest habitat (ESH) and associated wildlife species in the northeastern United States are in decline. One way to help create early successional forest conditions is engaging private forest landowners in even-aged forest management because their limited participation may have contributed to declines in ESH for wildlife species of high conservation concern. We applied the reasoned action approach from social psychology to predict intentions of landowners in the 13-county Southern Tier of New York State, USA, to conduct patch-cuts, which is a type of even-aged forest management. We tested the predictive ability of the model using data from a mail survey of landowners conducted from November 2010 to January 2011. Landowner intention to conduct patch-cuts was high (55% of respondents), with attitude being the strongest direct predictor of behavioral intention. Our results suggest that patch-cutting intentions are most likely expressed by landowners who think the behavior is good for their land and wildlife, believe in positive outcomes of land and wildlife management, belong to a game wildlife organization, and have conducted patch-cuts in the past. Strategies to engage more landowners in ESH management will have the highest likelihood of success if outreach efforts focus on influencing behavioral beliefs and subsequently attitudes, possibly working with game wildlife organizations to communicate a unified message for habitat conservation, including the importance of maintaining and creating ESH. Our results demonstrate the importance of social science research to increase the likelihood that conservation targets for declining wildlife species are met. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
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capture-recapture","interactions":[],"lastModifiedDate":"2016-06-16T11:30:22","indexId":"70173698","displayToPublicDate":"2016-06-07T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Estimating population density and connectivity of American mink using spatial capture-recapture","docAbstract":"Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture–recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture–recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km2 area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture–recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0315","usgsCitation":"Fuller, A.K., Sutherland, C.S., Royle, A., and Hare, M.P., 2016, Estimating population density and connectivity of American mink using spatial capture-recapture: Ecological Applications, v. 26, no. 4, p. 1125-1135, https://doi.org/10.1890/15-0315.","productDescription":"11 p.","startPage":"1125","endPage":"1135","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060020","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-08","publicationStatus":"PW","scienceBaseUri":"5757e21ee4b04f417c24269a","contributors":{"authors":[{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":637520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutherland, Christopher S.","contributorId":139375,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":637521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":637522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hare, Matthew P.","contributorId":171454,"corporation":false,"usgs":false,"family":"Hare","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":637523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173702,"text":"70173702 - 2016 - Seeing the forest through the trees: Considering roost-site selection at multiple spatial scales","interactions":[],"lastModifiedDate":"2016-06-07T12:52:20","indexId":"70173702","displayToPublicDate":"2016-06-07T13:45:00","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":"Seeing the forest through the trees: Considering roost-site selection at multiple spatial scales","docAbstract":"Conservation of bat species is one of the most daunting wildlife conservation challenges in North America, requiring detailed knowledge about their ecology to guide conservation efforts. Outside of the hibernating season, bats in temperate forest environments spend their diurnal time in day-roosts. In addition to simple shelter, summer roost availability is as critical as maternity sites and maintaining social group contact. To date, a major focus of bat conservation has concentrated on conserving individual roost sites, with comparatively less focus on the role that broader habitat conditions contribute towards roost-site selection. We evaluated roost-site selection by a northern population of federally-endangered Indiana bats (Myotis sodalis) at Fort Drum Military Installation in New York, USA at three different spatial scales: landscape, forest stand, and individual tree level. During 2007–2011, we radiotracked 33 Indiana bats (10 males, 23 females) and located 348 roosting events in 116 unique roost trees. At the landscape scale, bat roost-site selection was positively associated with northern mixed forest, increased slope, and greater distance from human development. At the stand scale, we observed subtle differences in roost site selection based on sex and season, but roost selection was generally positively associated with larger stands with a higher basal area, larger tree diameter, and a greater sugar maple (Acer saccharum) component. We observed no distinct trends of roosts being near high-quality foraging areas of water and forest edges. At the tree scale, roosts were typically in American elm (Ulmus americana) or sugar maple of large diameter (>30 cm) of moderate decay with loose bark. Collectively, our results highlight the importance of considering day roost needs simultaneously across multiple spatial scales. Size and decay class of individual roosts are key ecological attributes for the Indiana bat, however, larger-scale stand structural components that are products of past and current land use interacting with environmental aspects such as landform also are important factors influencing roost-tree selection patterns.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0150011","usgsCitation":"Jachowski, D.S., Rota, C., Dobony, C.A., Ford, W.M., and Edwards, J.W., 2016, Seeing the forest through the trees: Considering roost-site selection at multiple spatial scales: PLoS ONE, v. 11, no. 3, e0150011; 19 p., https://doi.org/10.1371/journal.pone.0150011.","productDescription":"e0150011; 19 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064338","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0150011","text":"Publisher Index Page"},{"id":323107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-30","publicationStatus":"PW","scienceBaseUri":"5757e220e4b04f417c2426b0","contributors":{"authors":[{"text":"Jachowski, David S.","contributorId":82966,"corporation":false,"usgs":true,"family":"Jachowski","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":637531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rota, Christopher T.","contributorId":92547,"corporation":false,"usgs":true,"family":"Rota","given":"Christopher T.","affiliations":[],"preferred":false,"id":637532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dobony, Christopher A.","contributorId":171455,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":637533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark wford@usgs.gov","contributorId":169828,"corporation":false,"usgs":false,"family":"Ford","given":"W.","email":"wford@usgs.gov","middleInitial":"Mark","affiliations":[],"preferred":false,"id":637528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, John W.","contributorId":169827,"corporation":false,"usgs":false,"family":"Edwards","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":637534,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173635,"text":"70173635 - 2016 - Age-specific survival of reintroduced swift fox in Badlands National Park and surrounding lands","interactions":[],"lastModifiedDate":"2016-06-24T11:41:01","indexId":"70173635","displayToPublicDate":"2016-06-07T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Age-specific survival of reintroduced swift fox in Badlands National Park and surrounding lands","docAbstract":"In 2003, a reintroduction program was initiated at Badlands National Park (BNP), South Dakota, USA, with swift foxes (Vulpes velox) translocated from Colorado and Wyoming, USA, as part of a restoration effort to recover declining swift fox populations throughout its historical range. Estimates of age-specific survival are necessary to evaluate the potential for population growth of reintroduced populations. We used 7 years (2003–2009) of capture–recapture data of 243 pups, 29 yearlings, and 69 adult swift foxes at BNP and the surrounding area to construct Cormack–Jolly–Seber model estimates of apparent survival within a capture–mark–recapture framework using Program MARK. The best model for estimating recapture probabilities included no differences among age classes, greater recapture probabilities during early years of the monitoring effort than later years, and variation among spring, winter, and summer. Our top ranked survival model indicated pup survival differed from that of yearlings and adults and varied by month and year. The apparent annual survival probability of pups (0.47, SE = 0.10) in our study area was greater than the apparent annual survival probability of yearlings and adults (0.27, SE = 0.08). Our results indicate low survival probabilities for a reintroduced population of swift foxes in the BNP and surrounding areas. Management of reintroduced populations and future reintroductions of swift foxes should consider the effects of relative low annual survival on population demography.
","language":"English","publisher":"Wiley Online Library","doi":"10.1002/wsb.641","usgsCitation":"Sasmal, I., Klaver, R.W., Jenks, J., and Schroeder, G.M., 2016, Age-specific survival of reintroduced swift fox in Badlands National Park and surrounding lands: Wildlife Society Bulletin, v. 40, no. 2, p. 217-223, https://doi.org/10.1002/wsb.641.","productDescription":"7 p.","startPage":"217","endPage":"223","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054537","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470906,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/224","text":"External Repository"},{"id":323098,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-17","publicationStatus":"PW","scienceBaseUri":"5757e21de4b04f417c242687","contributors":{"authors":[{"text":"Sasmal, Indrani","contributorId":52826,"corporation":false,"usgs":true,"family":"Sasmal","given":"Indrani","email":"","affiliations":[],"preferred":false,"id":637435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":637428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenks, Jonathan A.","contributorId":51591,"corporation":false,"usgs":true,"family":"Jenks","given":"Jonathan A.","affiliations":[],"preferred":false,"id":637436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schroeder, Greg M.","contributorId":54845,"corporation":false,"usgs":true,"family":"Schroeder","given":"Greg","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637437,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170488,"text":"sir20165043 - 2016 - Flood-Inundation Maps for Sugar Creek at Crawfordsville, Indiana","interactions":[],"lastModifiedDate":"2016-06-08T10:45:32","indexId":"sir20165043","displayToPublicDate":"2016-06-06T15:30:00","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":"2016-5043","title":"Flood-Inundation Maps for Sugar Creek at Crawfordsville, Indiana","docAbstract":"Digital flood-inundation maps for a 6.5-mile reach of Sugar Creek at Crawfordsville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind. Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS site CRWI3).
Flood profiles were computed for the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind., reach by means of a one-dimensional step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current stage-discharge rating at the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind., and high-water marks from the flood of April 19, 2013, which reached a stage of 15.3 feet. The hydraulic model was then used to compute 13 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 4.0 ft (the NWS “action stage”) to 16.0 ft, which is the highest stage interval of the current USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar]) data having a 0.49-ft root mean squared error and 4.9-ft horizontal resolution) to delineate the area flooded at each stage.
The availability of these maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165043","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Martin, Z.W., 2016, Flood-inundation maps for Sugar Creek at Crawfordsville, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5043, 11 p., https://dx.doi.org/10.3133/sir20165043.","productDescription":"Report: vi, 11 p.; Metadata; Spatial Data","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068569","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":322125,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5043/coverthb.jpg"},{"id":322126,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5043/sir20165043.pdf","text":"Report","size":"8.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5043"},{"id":322129,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5043/downloads/sir20165043_metadata_depthgrids.txt","text":"Depth Grids","size":"16.1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5043"},{"id":322130,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5043/downloads/sir20165043_metadata_shapefiles.txt ","text":"Shapefiles","size":"17.6 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5043"},{"id":322131,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5043/downloads/sir20165043_shapefiles.zip","text":"Shapefiles","size":"1.50 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5043"},{"id":322132,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5043/downloads/sir20165043_depthgrids.zip","text":"Depth Grids","size":"11.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5043"}],"country":"United States","state":"Indiana","city":"Crawfordsville","otherGeospatial":"Sugar Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.956787109375,\n 40.04115213981706\n ],\n [\n -86.95318222045898,\n 40.035369372460266\n ],\n [\n -86.89807891845703,\n 40.04548889350432\n ],\n [\n -86.88434600830078,\n 40.07557573609214\n ],\n [\n -86.89498901367188,\n 40.07807142745009\n ],\n [\n -86.9073486328125,\n 40.05442436453555\n ],\n [\n -86.92811965942383,\n 40.052322006146916\n ],\n [\n -86.956787109375,\n 40.04115213981706\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana-Kentucky Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
http://in.water.usgs.gov/
Information is sparse about aspects of female wolf (Canis lupus) breeding in the wild, including age of first reproduction, mean age of primiparity, generation time, and proportion of each age that breeds in any given year. We studied these subjects in 86 wolves (113 captures) in the Superior National Forest (SNF), Minnesota (MN), during 1972–2013 where wolves were legally protected for most of the period, and in 159 harvested wolves from throughout MN wolf range during 2012–2014. Breeding status of SNF wolves were assessed via nipple measurements, and wolves from throughout MN wolf range, by placental scars. In the SNF, proportions of currently breeding females (those breeding in the year sampled) ranged from 19% at age 2 to 80% at age 5, and from throughout wolf range, from 33% at age 2 to 100% at age 7. Excluding pups and yearlings, only 33% to 36% of SNF females and 58% of females from throughout MN wolf range bred in any given year. Generation time for SNF wolves was 4.3 years and for MN wolf range, 4.7 years. These findings will be useful in modeling wolf population dynamics and in wolf genetic and dog-domestication studies.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0156682","usgsCitation":"Mech, L.D., Barber-Meyer, S., and Erb, J., 2016, Wolf (Canis lupus) generation time and proportion of current breeding females by age: PLoS ONE, v. 11, no. 6, p. 1-13, https://doi.org/10.1371/journal.pone.0156682.","productDescription":"e0156682; 13 p.","startPage":"1","endPage":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066490","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470909,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0156682","text":"Publisher Index Page"},{"id":322186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Superior National Forest ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.703125,\n 48.09275716032736\n ],\n [\n -90.263671875,\n 48.10743118848039\n ],\n [\n -90.04394531249999,\n 48.1367666796927\n ],\n [\n -89.93408203124999,\n 48.004625021133904\n ],\n [\n -89.67041015625,\n 48.004625021133904\n ],\n [\n -89.80224609374999,\n 47.90161354142075\n ],\n [\n -90.50537109375,\n 47.724544549099676\n ],\n [\n -90.966796875,\n 47.47266286861342\n ],\n [\n -91.1865234375,\n 47.29413372501023\n ],\n [\n -91.95556640625,\n 46.9052455464292\n ],\n [\n -92.04345703125,\n 46.7549166192819\n ],\n [\n -92.35107421874999,\n 46.66451741754235\n ],\n [\n -92.63671875,\n 46.483264729155586\n ],\n [\n -93.1640625,\n 46.483264729155586\n ],\n [\n -93.8671875,\n 46.437856895024204\n ],\n [\n -94.46044921875,\n 46.49839225859763\n ],\n [\n -95.537109375,\n 46.51351558059737\n ],\n [\n -95.99853515625,\n 46.7549166192819\n ],\n [\n -95.8447265625,\n 47.30903424774781\n ],\n [\n -95.9326171875,\n 47.76886840424207\n ],\n [\n -95.91064453125,\n 47.97521412341618\n ],\n [\n -95.625,\n 48.122101028190805\n ],\n [\n -95.4052734375,\n 48.356249029540734\n ],\n [\n -95.03173828125,\n 48.60385760823255\n ],\n [\n -94.7021484375,\n 48.76343113791796\n ],\n [\n -94.4384765625,\n 48.705462895790575\n ],\n [\n -94.19677734375,\n 48.66194284607006\n ],\n [\n -93.9990234375,\n 48.66194284607006\n ],\n [\n -93.80126953124999,\n 48.56024979174329\n ],\n [\n -93.53759765625,\n 48.545705491847464\n ],\n [\n -93.40576171875,\n 48.63290858589532\n ],\n [\n -93.1640625,\n 48.66194284607006\n ],\n [\n -92.900390625,\n 48.66194284607006\n ],\n [\n -92.57080078125,\n 48.545705491847464\n ],\n [\n -92.6806640625,\n 48.50204750525715\n ],\n [\n -92.57080078125,\n 48.44377831058805\n ],\n [\n -92.373046875,\n 48.25394114463431\n ],\n [\n -92.26318359375,\n 48.3416461723746\n ],\n [\n -92.10937499999999,\n 48.356249029540734\n ],\n [\n -91.91162109375,\n 48.151428143221224\n ],\n [\n -91.58203125,\n 48.10743118848039\n ],\n [\n -91.47216796875,\n 48.09275716032736\n ],\n [\n -91.1865234375,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.23930899024905\n ],\n [\n -90.72509765625,\n 48.16608541901253\n ],\n [\n -90.703125,\n 48.09275716032736\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-03","publicationStatus":"PW","scienceBaseUri":"5756909ee4b023b96ec20aa4","contributors":{"authors":[{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":631857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber-Meyer, Shannon M. 0000-0002-3048-2616 sbarber-meyer@usgs.gov","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":4422,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon M.","email":"sbarber-meyer@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":631858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erb, John","contributorId":170057,"corporation":false,"usgs":false,"family":"Erb","given":"John","email":"","affiliations":[],"preferred":false,"id":631859,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178697,"text":"70178697 - 2016 - The Montaguto earth flow: nine years of observation and analysis","interactions":[],"lastModifiedDate":"2016-12-20T14:03:57","indexId":"70178697","displayToPublicDate":"2016-06-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The Montaguto earth flow: nine years of observation and analysis","docAbstract":"This paper summarizes the methods, results, and interpretation of analyses carried out between 2006 and 2015 at the Montaguto earth flow in southern Italy. We conducted a multi-temporal analysis of earth-flow activity to reconstruct the morphological and structural evolution of the flow. Data from field mapping were combined with a geometric reconstruction of the basal slip surface in order to investigate relations between basal-slip surface geometry and deformation styles of earth-flow material. Moreover, we reconstructed the long-term pattern of earth-flow movement using both historical observations and modeled hydrologic and climatic data. Hydrologic and climatic data were used to develop a Landslide Hydrological Climatological (LHC) indicator model.","largerWorkTitle":"Proceedings of the 12th International Symposium on Landslides","conferenceTitle":"12th International Symposium on Landslides","conferenceDate":"12-19 June 2016","conferenceLocation":"Napoli, Italy","language":"English","publisher":"CRC Press","doi":"10.1201/b21520-123","usgsCitation":"Guerriero, L., Revellino, R., Grelle, G., Diodato, N., Guadagno, F., and Coe, J.A., 2016, The Montaguto earth flow: nine years of observation and analysis, in Proceedings of the 12th International Symposium on Landslides, Napoli, Italy, 12-19 June 2016, p. 1035-1042, https://doi.org/10.1201/b21520-123.","productDescription":"8 p.","startPage":"1035","endPage":"1042","ipdsId":"IP-072568","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":332348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331456,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/Landslides-and-Engineered-Slopes-Experience-Theory-and-Practice-Proceedings/Aversa-Cascini-Picarelli-Scavia/p/book/9781138029880"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-09","publicationStatus":"PW","scienceBaseUri":"585a51bee4b01224f329b5e9","contributors":{"authors":[{"text":"Guerriero, L.","contributorId":177147,"corporation":false,"usgs":false,"family":"Guerriero","given":"L.","affiliations":[],"preferred":false,"id":654853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Revellino, R","contributorId":177169,"corporation":false,"usgs":false,"family":"Revellino","given":"R","email":"","affiliations":[],"preferred":false,"id":654854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grelle, G.","contributorId":177149,"corporation":false,"usgs":false,"family":"Grelle","given":"G.","affiliations":[],"preferred":false,"id":654855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diodato, N","contributorId":177170,"corporation":false,"usgs":false,"family":"Diodato","given":"N","email":"","affiliations":[],"preferred":false,"id":654856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guadagno, F.M.","contributorId":177171,"corporation":false,"usgs":false,"family":"Guadagno","given":"F.M.","affiliations":[],"preferred":false,"id":654857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":654858,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174958,"text":"70174958 - 2016 - Origin and dynamics of depositionary subduction margins","interactions":[],"lastModifiedDate":"2016-07-22T15:59:27","indexId":"70174958","displayToPublicDate":"2016-06-05T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Origin and dynamics of depositionary subduction margins","docAbstract":"Here we propose a new framework for forearc evolution that focuses on the potential feedbacks between subduction tectonics, sedimentation, and geomorphology that take place during an extreme event of subduction erosion. These feedbacks can lead to the creation of a “depositionary forearc,” a forearc structure that extends the traditional division of forearcs into accretionary or erosive subduction margins by demonstrating a mode of rapid basin accretion during an erosive event at a subduction margin. A depositionary mode of forearc evolution occurs when terrigenous sediments are deposited directly on the forearc while it is being removed from below by subduction erosion. In the most extreme case, an entire forearc can be removed by a single subduction erosion event followed by depositionary replacement without involving transfer of sediments from the incoming plate. We need to further recognize that subduction forearcs are often shaped by interactions between slow, long-term processes, and sudden extreme events reflecting the sudden influences of large-scale morphological variations in the incoming plate. Both types of processes contribute to the large-scale architecture of the forearc, with extreme events associated with a replacive depositionary mode that rapidly creates sections of a typical forearc margin. The persistent upward diversion of the megathrust is likely to affect its geometry, frictional nature, and hydrogeology. Therefore, the stresses along the fault and individual earthquake rupture characteristics are also expected to be more variable in these erosive systems than in systems with long-lived megathrust surfaces.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GC006259","usgsCitation":"Vannucchi, P., Morgan, J.P., Silver, E., and Kluesner, J.W., 2016, Origin and dynamics of depositionary subduction margins: Geochemistry, Geophysics, Geosystems, v. 17, no. 6, p. 1966-1974, https://doi.org/10.1002/2016GC006259.","productDescription":"9 p.","startPage":"1966","endPage":"1974","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071075","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470911,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gc006259","text":"Publisher Index Page"},{"id":325564,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-05","publicationStatus":"PW","scienceBaseUri":"57934449e4b0eb1ce79e8c09","contributors":{"authors":[{"text":"Vannucchi, Paola","contributorId":173112,"corporation":false,"usgs":false,"family":"Vannucchi","given":"Paola","email":"","affiliations":[{"id":27154,"text":"Royal Holloway, University of London","active":true,"usgs":false}],"preferred":false,"id":643349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morgan, Jason P.","contributorId":173113,"corporation":false,"usgs":false,"family":"Morgan","given":"Jason","email":"","middleInitial":"P.","affiliations":[{"id":27154,"text":"Royal Holloway, University of London","active":true,"usgs":false}],"preferred":false,"id":643350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silver, Eli","contributorId":173114,"corporation":false,"usgs":false,"family":"Silver","given":"Eli","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":643351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":167088,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":643348,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171548,"text":"70171548 - 2016 - Flexible characterization of animal movement pattern using net squared displacement and a latent state model","interactions":[],"lastModifiedDate":"2016-06-03T11:14:39","indexId":"70171548","displayToPublicDate":"2016-06-03T06:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Flexible characterization of animal movement pattern using net squared displacement and a latent state model","docAbstract":"Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucidate variability in the movement strategies of eight giant tortoises (Chelonoidis sp.) using a multi-year (2009–2014) GPS dataset from three different Galapagos Islands.
\nWith respect to patterns of time spent and the number of transitions between modes, our approach out-performed previous efforts to distinguish among migration, dispersal, and sedentary behavior. We documented marked inter-individual variation in giant tortoise movement strategies, with behaviors indicating migration, dispersal, nomadism and sedentarism, as well as hybrid behaviors such as “exploratory residence”.
\nDistilling complex animal movement into discrete modes remains a fundamental challenge in movement ecology, a problem made more complex by the ever-longer duration, ever-finer resolution, and gap-ridden trajectories recorded by GPS devices. By clustering into modes, we derived information on the time spent within one mode and the number of transitions between modes which enabled finer differentiation of movement strategies over previous methods. Ultimately, the techniques developed here address limitations of previous approaches and provide greater insights with respect to characterization of movement strategies across scales by more fully utilizing long-term GPS telemetry datasets.
\n\n
Conservation of imperiled species often requires knowledge of vital rates and population dynamics. However, these can be difficult to estimate for rare species and small populations. This problem is further exacerbated when individuals are not available for detection during some surveys due to limited access, delaying surveys and creating mismatches between the breeding behavior and survey timing. Here we use simulations to explore the impacts of this issue using four hypothetical boreal toad (Anaxyrus boreas boreas) populations, representing combinations of logistical access (accessible, inaccessible) and breeding behavior (synchronous, asynchronous). We examine the bias and precision of survival and breeding probability estimates generated by survey designs that differ in effort and timing for these populations. Our findings indicate that the logistical access of a site and mismatch between the breeding behavior and survey design can greatly limit the ability to yield accurate and precise estimates of survival and breeding probabilities. Simulations similar to what we have performed can help researchers determine an optimal survey design(s) for their system before initiating sampling efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2016.05.006","usgsCitation":"Lanier, W.E., Bailey, L., and Muths, E.L., 2016, Integrating biology, field logistics, and simulations to optimize parameter estimation for imperiled species: Ecological Modelling, v. 335, p. 16-23, https://doi.org/10.1016/j.ecolmodel.2016.05.006.","productDescription":"8 p.","startPage":"16","endPage":"23","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064688","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":322090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"335","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57514a9de4b053f0edd01768","contributors":{"authors":[{"text":"Lanier, Wendy E.","contributorId":9013,"corporation":false,"usgs":true,"family":"Lanier","given":"Wendy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":626151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Larissa L.","contributorId":93183,"corporation":false,"usgs":true,"family":"Bailey","given":"Larissa L.","affiliations":[],"preferred":false,"id":626152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":626150,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270642,"text":"70270642 - 2016 - Geometric quality assessment of lidar data based on swath overlap","interactions":[],"lastModifiedDate":"2025-08-21T15:16:44.549768","indexId":"70270642","displayToPublicDate":"2016-06-02T10:00:54","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geometric quality assessment of lidar data based on swath overlap","docAbstract":"This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the inter-swath errors, including the presence of systematic errors in lidar data. Hence they pose a risk to the user in terms of data acceptance (i.e. a higher potential for Type II error indicating risk of accepting potentially unsuitable data). For example, if the overlap area is too small or if the sampled locations are close to the center of overlap, or if the errors are sampled in flat regions when there are residual pitch errors in the data, the resultant Root Mean Square Differences (RMSD) can still be small. To avoid this, the following are suggested to be used as criteria for defining the inter-swath quality of data:
a) Median Discrepancy Angle
b) Mean and RMSD of Horizontal Errors using DQM measured on sloping surfaces
c) RMSD for sampled locations from flat areas (defined as areas with less than 5 degrees of slope)
It is suggested that 4000-5000 points are uniformly sampled in the overlapping regions of the point cloud, and depending on the surface roughness, to measure the discrepancy between swaths. Care must be taken to sample only areas of single return points only. Point-to-Plane distance based data quality measures are determined for each sample point. These measurements are used to determine the above mentioned parameters. This paper details the measurements and analysis of measurements required to determine these metrics, i.e. Discrepancy Angle, Mean and RMSD of errors in flat regions and horizontal errors obtained using measurements extracted from sloping regions (slope greater than 10 degrees). The research is a result of an ad-hoc joint working group of the US Geological Survey and the American Society for Photogrammetry and Remote Sensing (ASPRS) Airborne Lidar Committee.
The magnitude 4.0 earthquake that occurred on October 16, 2012, near Hollis Center and Waterboro in southwestern Maine surprised and startled local residents but caused only minor damage. A two-person U.S. Geological Survey (USGS) team was sent to Maine to conduct an intensity survey and document the damage. The only damage we observed was the failure of a chimney and plaster cracks in two buildings in East and North Waterboro, 6 kilometers (km) west of the epicenter. We photographed the damage and interviewed residents to determine the intensity distribution in the epicentral area. The damage and shaking reports are consistent with a maximum Modified Mercalli Intensity (MMI) of 5–6 for an area 1–8 km west of the epicenter, slightly higher than the maximum Community Decimal Intensity (CDI) of 5 determined by the USGS “Did You Feel It?” Web site. The area of strong shaking in East Waterboro corresponds to updip rupture on a fault plane that dips steeply east.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161071","usgsCitation":"Radakovich, A.L., Ferguson, A.J., and Boatwright, John, 2016, Field survey of earthquake effects from the magnitude 4.0 southern Maine earthquake of October 16, 2012: U.S. Geological Survey Open-File Report 2016–1071, 17 p., https://dx.doi.org/10.3133/ofr20161071. ","productDescription":"iv, 17 p.","startPage":"1","endPage":"17","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-046067","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":322068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1071/ofr20161071.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1071"},{"id":322067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1071/coverthb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76,\n 41\n ],\n [\n -76,\n 46\n ],\n [\n -68.5,\n 46\n ],\n [\n -68.5,\n 41\n ],\n [\n -76,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
http://earthquake.usgs.gov/
Two test wells were completed at the Barbour Pointe community in western Chatham County, near Savannah, Georgia, in 2013 to investigate the potential of using the Lower Floridan aquifer as a source of municipal water supply. One well was completed in the Lower Floridan aquifer at a depth of 1,080 feet (ft) below land surface; the other well was completed in the Upper Floridan aquifer at a depth of 440 ft below land surface. At the Barbour Pointe test site, the U.S. Geological Survey completed electromagnetic (EM) flowmeter surveys, collected and analyzed water samples from discrete depths, and completed a 72-hour aquifer test of the Floridan aquifer system withdrawing from the Lower Floridan aquifer.
Based on drill cuttings, geophysical logs, and borehole EM flowmeter surveys collected at the Barbour Pointe test site, the Upper Floridan aquifer extends 369 to 567 ft below land surface, the middle semiconfining unit, separating the two aquifers, extends 567 to 714 ft below land surface, and the Lower Floridan aquifer extends 714 to 1,056 ft below land surface.
A borehole EM flowmeter survey indicates that the Upper Floridan and Lower Floridan aquifers each contain four water-bearing zones. The EM flowmeter logs of the test hole open to the entire Floridan aquifer system indicated that the Upper Floridan aquifer contributed 91 percent of the total flow rate of 1,000 gallons per minute; the Lower Floridan aquifer contributed about 8 percent. Based on the transmissivity of the middle semiconfining unit and the Floridan aquifer system, the middle semiconfining unit probably contributed on the order of 1 percent of the total flow.
Hydraulic properties of the Upper Floridan and Lower Floridan aquifers were estimated based on results of the EM flowmeter survey and a 72-hour aquifer test completed in Lower Floridan aquifer well 36Q398. The EM flowmeter data were analyzed using an AnalyzeHOLE-generated model to simulate upward borehole flow and determine the transmissivity of water-bearing zones. Aquifer-test data were analyzed with a two-dimensional, axisymmetric, radial, transient, groundwater-flow model using MODFLOW–2005. The flowmeter-survey and aquifer-test simulations provided an estimated transmissivity of about 60,000 square feet per day for the Upper Floridan aquifer and about 5,000 square feet per day for the Lower Floridan aquifer.
Water in discrete-depth samples collected from the Upper Floridan aquifer, middle semiconfining unit, and Lower Floridan aquifer during the EM flowmeter survey in August 2013 was low in dissolved solids. Tested constituents were in concentrations within established U.S. Environmental Protection Agency drinking water-quality criteria. Concentrations of measured constituents in water samples from Lower Floridan aquifer well 36Q398 collected at the end of the 72-hour aquifer test in November 2013 were generally higher than in the discrete-depth samples collected during EM flowmeter testing in August 2013 but remained within established drinking water-quality criteria.
Water-level data for the aquifer test were filtered for external influences such as barometric pressure, earth-tide effects, and long-term trends to enable detection of small (less than 1 ft) water-level responses to aquifer-test withdrawal. During the 72-hour aquifer test, the Lower Floridan aquifer was pumped at a rate of 750 gallons per minute resulting in a drawdown response of 35.5 ft in the pumped well; 1.6 ft in the Lower Floridan aquifer observation well located about 6,000 ft west of the pumped well; and responses of 0.7, 0.6, and 0.4 ft in the Upper Floridan aquifer observation wells located about 36 ft, 6,000 ft, and 6,800 ft from the pumped well, respectively
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165028","collaboration":"Prepared in cooperation with Consolidated Utilities LLC, Chatham County, Georgia","usgsCitation":"Gonthier, G.J., and Clarke, J.S., 2016, Hydrogeology and water quality of the Floridan aquifer system and effect of Lower Floridan aquifer withdrawals on the Upper Floridan aquifer at Barbour Pointe Community, Chatham County, Georgia, 2013: U.S. Geological Survey Scientific Investigations Report 2016–5028, 56 p., https://dx.doi.org/10.3133/sir20165028.","productDescription":"viii, 56 p.","startPage":"1","endPage":"56","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-045188","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":321737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5028/coverthb.jpg"},{"id":321738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5028/sir20165028.pdf","text":"Report","size":"1.68 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5028"}],"country":"United States","state":"Georgia","county":"Chatham County","city":"Savannah","otherGeospatial":"Barbour Pointe Community","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.75,\n 32.25\n ],\n [\n -80.75,\n 31.75\n ],\n [\n -81.75,\n 31.75\n ],\n [\n -81.75,\n 32.25\n ],\n [\n -80.75,\n 32.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Georgia Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, Georgia 30093
The GIS Flood Tool (GFT) was developed by the U.S. Geological Survey with support from the U.S. Agency for International Development’s Office of U.S. Foreign Disaster Assistance to provide a means for production of reconnaissance-level flood inundation mapping for data-sparse and resource-limited areas of the world. The GFT has also attracted interest as a tool for rapid assessment flood inundation mapping for the Flood Inundation Mapping Program of the U.S. Geological Survey. The GFT can fill an important gap for communities that lack flood inundation mapping by providing a first-estimate of inundation zones, pending availability of resources to complete an engineering study. The tool can also help identify priority areas for application of scarce flood inundation mapping resources. The technical basis of the GFT is an application of the Manning equation for steady flow in an open channel, operating on specially processed digital elevation data. The GFT is implemented as a software extension in ArcGIS. Output maps from the GFT were validated at 11 sites with inundation maps produced previously by the Flood Inundation Mapping Program using standard one-dimensional hydraulic modeling techniques. In 80 percent of the cases, the GFT inundation patterns matched 75 percent or more of the one-dimensional hydraulic model inundation patterns. Lower rates of pattern agreement were seen at sites with low relief and subtle surface water divides. Although the GFT is simple to use, it should be applied with the oversight or review of a qualified hydraulic engineer who understands the simplifying assumptions of the approach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161038","collaboration":"Prepared in cooperation with the U.S. Agency for International Development, Office of U.S. Foreign Disaster Assistance (USAID/OFDA)","usgsCitation":"Verdin, James; Verdin, Kristine; Mathis, Melissa; Magadzire, Tamuka; Kabuchanga, Eric; Woodbury, Mark; and Gadain, Hussein, 2016, A software tool for rapid flood inundation mapping: U.S. Geological Survey Open-File Report 2016–1038, 26 p., https://dx.doi.org/10.3133/ofr20161038.","productDescription":"vi, 26 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055868","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":322105,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1038/ofr20161038.pdf","text":"Report","size":"16.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1038"},{"id":322104,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1038/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
Book review of Hobbs and Hooten 2015 Bayesian models: a statistical primer for ecologists.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Brooklyn, NY","doi":"10.1002/ecy.1367","usgsCitation":"Irvine, K.M., 2016, Finally, the magic of Bayesian model notation revealed: Ecology, v. 97, no. 6, p. 1629-1630, https://doi.org/10.1002/ecy.1367.","startPage":"1629","endPage":"1630","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071252","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":325987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a1c42fe4b006cb45552c14","contributors":{"authors":[{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644348,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70160311,"text":"70160311 - 2016 - A fault-based model for crustal deformation, fault slip-rates and off-fault strain rate in California","interactions":[],"lastModifiedDate":"2016-06-01T13:34:44","indexId":"70160311","displayToPublicDate":"2016-06-01T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"A fault-based model for crustal deformation, fault slip-rates and off-fault strain rate in California","docAbstract":"We invert Global Positioning System (GPS) velocity data to estimate fault slip rates in California using a fault‐based crustal deformation model with geologic constraints. The model assumes buried elastic dislocations across the region using Uniform California Earthquake Rupture Forecast Version 3 (UCERF3) fault geometries. New GPS velocity and geologic slip‐rate data were compiled by the UCERF3 deformation working group. The result of least‐squares inversion shows that the San Andreas fault slips at 19–22 mm/yr along Santa Cruz to the North Coast, 25–28 mm/yr along the central California creeping segment to the Carrizo Plain, 20–22 mm/yr along the Mojave, and 20–24 mm/yr along the Coachella to the Imperial Valley. Modeled slip rates are 7–16 mm/yr lower than the preferred geologic rates from the central California creeping section to the San Bernardino North section. For the Bartlett Springs section, fault slip rates of 7–9 mm/yr fall within the geologic bounds but are twice the preferred geologic rates. For the central and eastern Garlock, inverted slip rates of 7.5 and 4.9 mm/yr, respectively, match closely with the geologic rates. For the western Garlock, however, our result suggests a low slip rate of 1.7 mm/yr. Along the eastern California shear zone and southern Walker Lane, our model shows a cumulative slip rate of 6.2–6.9 mm/yr across its east–west transects, which is ∼1 mm/yr increase of the geologic estimates. For the off‐coast faults of central California, from Hosgri to San Gregorio, fault slips are modeled at 1–5 mm/yr, similar to the lower geologic bounds. For the off‐fault deformation, the total moment rate amounts to 0.88×1019 N·m/yr, with fast straining regions found around the Mendocino triple junction, Transverse Ranges and Garlock fault zones, Landers and Brawley seismic zones, and farther south. The overall California moment rate is 2.76×1019 N·m/yr, which is a 16% increase compared with the UCERF2 model.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120140250","usgsCitation":"Zeng, Y., and Shen, Z., 2016, A fault-based model for crustal deformation, fault slip-rates and off-fault strain rate in California: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 766-784, https://doi.org/10.1785/0120140250.","productDescription":"19 p.","startPage":"766","endPage":"784","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071033","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":322021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-15","publicationStatus":"PW","scienceBaseUri":"574ff91ae4b0ee97d51af4c9","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":582499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shen, Zheng-Kang","contributorId":145691,"corporation":false,"usgs":false,"family":"Shen","given":"Zheng-Kang","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":582500,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70171472,"text":"70171472 - 2016 - Changes to extreme wave climates of islands within the Western Tropical Pacific throughout the 21st century under RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability","interactions":[],"lastModifiedDate":"2016-06-01T08:57:30","indexId":"70171472","displayToPublicDate":"2016-06-01T09:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Changes to extreme wave climates of islands within the Western Tropical Pacific throughout the 21st century under RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability","docAbstract":"Waves are the dominant influence on coastal morphology and ecosystem structure of tropical Pacific islands. Wave heights, periods, and directions for the 21st century were projected using near-surface wind fields from four atmosphere-ocean coupled global climate models (GCM) under representative concentration pathways (RCP) 4.5 and 8.5. GCM-derived wind fields forced the global WAVEWATCH-III wave model to generate hourly time-series of bulk wave parameters around 25 islands in the mid to western tropical Pacific Ocean for historical (1976–2005), mid-, and end-of-century time periods. Extreme significant wave heights decreased (~10.0%) throughout the 21st century under both climate scenarios compared to historical wave conditions and the higher radiative forcing 8.5 scenario displayed a greater and more widespread decrease in extreme significant wave heights compared to the lower forcing 4.5 scenario. An exception was for the end-of-century June–August season. Offshore of islands in the central equatorial Pacific, extreme significant wave heights displayed the largest changes from historical values. The frequency of extreme events during December–February decreased under RCP 8.5, whereas the frequency increased under RCP 4.5. Mean wave directions often rotated more than 30° clockwise at several locations during June–August, which could indicate a weakening of the trade winds’ influence on extreme wave directions and increasing dominance of Southern Ocean swell or eastern shift of storm tracks. The projected changes in extreme wave heights, directions of extreme events, and frequencies at which extreme events occur will likely result in changes to the morphology and sustainability of island nations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2016.03.009","usgsCitation":"Shope, J.B., Storlazzi, C.D., Erikson, L.H., and Hegermiller, C., 2016, Changes to extreme wave climates of islands within the Western Tropical Pacific throughout the 21st century under RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability: Global and Planetary Change, v. 141, p. 25-38, https://doi.org/10.1016/j.gloplacha.2016.03.009.","productDescription":"14 p.","startPage":"25","endPage":"38","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067314","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":321950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"574ff91be4b0ee97d51af4d6","contributors":{"authors":[{"text":"Shope, James B.","contributorId":135949,"corporation":false,"usgs":false,"family":"Shope","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":631162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":631161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":631163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hegermiller, Christie 0000-0002-6383-7508 chegermiller@usgs.gov","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":149010,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","email":"chegermiller@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":631164,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170066,"text":"pp1826 - 2016 - Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of Alaska","interactions":[],"lastModifiedDate":"2022-04-22T14:21:36.662288","indexId":"pp1826","displayToPublicDate":"2016-06-01T09:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1826","title":"Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of Alaska","docAbstract":"This assessment was conducted to fulfill the requirements of section 712 of the Energy Independence and Security Act of 2007 and to contribute to knowledge of the storage, fluxes, and balance of carbon and methane gas in ecosystems of Alaska. The carbon and methane variables were examined for major terrestrial ecosystems (uplands and wetlands) and inland aquatic ecosystems in Alaska in two time periods: baseline (from 1950 through 2009) and future (projections from 2010 through 2099). The assessment used measured and observed data and remote sensing, statistical methods, and simulation models. The national assessment, conducted using the methodology described in SIR 2010-5233, has been completed for the conterminous United States, with results provided in three separate regional reports (PP 1804, PP 1797, and PP 1897).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1826","usgsCitation":"Zhu, Zhiliang, and McGuire, A.D., eds., 2016, Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of Alaska: U.S. Geological Survey Professional Paper 1826, 196 p., https://dx.doi.org/10.3133/pp1826.","productDescription":"Report: viii, 196 p.","numberOfPages":"208","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-066384","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":320417,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20105233","text":"Scientific Investigations Report 2010-5233","linkHelpText":"- A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios"},{"id":320416,"rank":17,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1787","text":"Professional Paper 1787","linkHelpText":"- Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in the Great Plains Region of the United States"},{"id":320415,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1797","text":"Professional Paper 1797","linkHelpText":"- Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Western United States"},{"id":320414,"rank":15,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1804","text":"Professional Paper 1804","linkHelpText":"- Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States"},{"id":334983,"rank":19,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SB43X2","text":"Alaska LandCarbon Wetland Distribution Map"},{"id":320412,"rank":14,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/pp/1826/pp1826_chapter9.pdf","text":"Chapter 9. Alaska Carbon Balance","size":"201 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1826","linkHelpText":"Land Change Science Program
U.S. Geological Survey
12201 Sunrise Valley Drive
MS 519A National Center
Reston, VA 20192
http://www.usgs.gov/climate_landuse/lcs/
The winter meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held January 12-14, 2016, at Virginia Tech University in Blacksburg, VA. LST co-chairs Tom Loveland [USGS’s Earth Resources Observation and Science Data Center (EROS)—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist] welcomed more than 50 participants to the three-day meeting. The main objectives of this meeting focused on identifying priorities and approaches to improve the global moderate-resolution satellite record. Overall, the meeting was geared more towards soliciting team member recommendations on several rapidly evolving issues, than on providing updates on individual research activities. All the presentations given at the meeting are available at landsat.usgs. gov//science_LST_january2016.php.
","language":"English","publisher":"NASA","usgsCitation":"Schroeder, T., Loveland, T., Wulder, M.A., and Irons, J.R., 2016, Landsat Science Team: 2016 winter meeting summary: The Earth Observer, p. 19-23.","productDescription":"5 p.","startPage":"19","endPage":"23","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074277","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":324210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324209,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/May_June_2016_color%20508.pdf"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576bb6b6e4b07657d1a228c9","contributors":{"authors":[{"text":"Schroeder, Todd tschroeder@usgs.gov","contributorId":149137,"corporation":false,"usgs":true,"family":"Schroeder","given":"Todd","email":"tschroeder@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":626045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":626046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":626047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":626048,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}