Hurricane Irma made landfall in south Florida, USA, on September 10, 2017 as a category 4 storm. In January 2018, fieldwork was conducted on four previously (2014) sampled islands in Florida Bay, Everglades National Park to examine changes between 2014 and 2018. The objectives were to determine if the net impact of the storm was gain or loss of island landmass and/or elevation; observe and quantify impacts to mangroves; and identify distinctive sedimentary, biochemical, and/or geochemical signatures of the storm. Storm overwash deposits were measured in the field and, in general, interior island mudflats appeared to experience deposition ranging from ~ 0.5 to ~ 6.5 cm. Elevation changes were measured using real-time kinematic positioning and satellite receivers. Comparison of 2014 to 2018 elevation measurements indicates mangrove berms and transitional areas between mudflats and berms experienced erosion and loss of elevation, whereas interior mudflats gained elevation, possibly due to Hurricane Irma. Geographic information system analysis of pre- and post-storm satellite imagery indicates the western-most island, closest to the eye of the storm, lost 32 to 42% (~ 11 to 13 m) of the width of the eastern berm, and vegetated coverage was reduced 9.3% or ~ 9700 m2. Vegetated coverage on the eastern-most island was reduced by 1.9% or ~ 9200 m2. These results are compared to previous accounts of hurricane impacts and provide a baseline for examining long-term constructive and destructive aspects of hurricanes on the islands and the role of storms in resiliency of Florida Bay islands.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00638-7","usgsCitation":"Wingard, G.L., Bergstresser, S.E., Stackhouse, B., Jones, M., Marot, M.E., Hoefke, K., Daniels, A., and Keller, K., 2020, Impacts of Hurricane Irma on Florida Bay Islands, Everglades National Park, U.S.A.: Estuaries and Coasts, v. 43, p. 1070-1089, https://doi.org/10.1007/s12237-019-00638-7.","productDescription":"20 p.","startPage":"1070","endPage":"1089","ipdsId":"IP-102008","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":458463,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-019-00638-7","text":"Publisher Index Page"},{"id":369564,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park, Florida Bay Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.903076171875,\n 24.56211235799689\n ],\n [\n -80.2001953125,\n 24.56211235799689\n ],\n [\n -80.2001953125,\n 25.311752681576287\n ],\n [\n -81.903076171875,\n 25.311752681576287\n ],\n [\n -81.903076171875,\n 24.56211235799689\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Wingard, G. 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In N limited systems, increases in N inputs may lead to excess productivity and hypoxia. Like many temperate estuaries, Long Island Sound (LIS), a major eastern U.S. estuary, is a N limited system which has experienced seasonal hypoxia since the 1800s. This study is the first effort to constrain the total N cycle in this estuary. The approach utilizes data collected over the last two decades in the LIS time series with hydrodynamic model results to generate both monthly and annual N budgets between 1995 and 2016. Of the total N that is delivered to LIS through rivers and atmospheric inputs, 40% is exported to the adjacent continental shelf on the order of 10.8 ± 8.9 × 106 kg N/year. Of this export, 41% is dissolved organic N, 29% is particulate organic N, 32% is nitrate + nitrite, and −3% is ammonium. The remaining 60% of the N delivered to LIS is either buried in sediments or lost through denitrification. This inferred internal loss rate is equivalent to 5.4 g N/(m2year). This study serves as an example of the significant inter-annual variations that estuarine budgets undergo as efforts to understand coastal biogeochemical cycles move forward.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2019.106493","usgsCitation":"Vlahos, P., Whitney, M., Menniti, C., Mullaney, J., Morrison, J., and Jia, Y., 2020, Nitrogen budgets of the Long Island Sound estuary: Estuarine, Coastal and Shelf Science, v. 232, 106493, 9 p., https://doi.org/10.1016/j.ecss.2019.106493.","productDescription":"106493, 9 p.","ipdsId":"IP-109478","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437196,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVXGBB","text":"USGS data release","linkHelpText":"Nitrogen concentrations and loads and seasonal nitrogen loads in selected Long Island Sound tributaries, water years 1995-2016"},{"id":382808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, New York","otherGeospatial":"Long Island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.8336181640625,\n 40.77638178482896\n ],\n [\n -73.63037109375,\n 40.81796653313175\n ],\n [\n -73.17993164062499,\n 40.88029480552824\n ],\n [\n -72.61962890625,\n 40.9218144123785\n ],\n [\n -72.3834228515625,\n 40.896905775860006\n ],\n [\n -71.8670654296875,\n 41.05864414643029\n ],\n [\n -71.553955078125,\n 41.15384235711447\n ],\n [\n -71.4605712890625,\n 41.413895564677304\n ],\n [\n -72.1856689453125,\n 41.31907562295139\n ],\n [\n -72.784423828125,\n 41.290189955885644\n ],\n [\n -72.9656982421875,\n 41.269549502842565\n ],\n [\n -73.3447265625,\n 41.1455697310095\n ],\n [\n -73.7677001953125,\n 40.97160353279909\n ],\n [\n -73.8720703125,\n 40.834593138080244\n ],\n [\n -73.8336181640625,\n 40.77638178482896\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"232","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vlahos, Penny","contributorId":191277,"corporation":false,"usgs":false,"family":"Vlahos","given":"Penny","email":"","affiliations":[],"preferred":false,"id":809411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitney, Michael 0000-0002-2048-7755","orcid":"https://orcid.org/0000-0002-2048-7755","contributorId":248577,"corporation":false,"usgs":false,"family":"Whitney","given":"Michael","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":809412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Menniti, Christina","contributorId":248578,"corporation":false,"usgs":false,"family":"Menniti","given":"Christina","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":809413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullaney, John R. 0000-0003-4936-5046","orcid":"https://orcid.org/0000-0003-4936-5046","contributorId":203254,"corporation":false,"usgs":true,"family":"Mullaney","given":"John R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809414,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrison, Jonathan 0000-0002-1756-4609 jmorriso@usgs.gov","orcid":"https://orcid.org/0000-0002-1756-4609","contributorId":2274,"corporation":false,"usgs":true,"family":"Morrison","given":"Jonathan","email":"jmorriso@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809417,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jia, Yan","contributorId":248579,"corporation":false,"usgs":false,"family":"Jia","given":"Yan","email":"","affiliations":[],"preferred":false,"id":809415,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70206881,"text":"70206881 - 2020 - Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","interactions":[],"lastModifiedDate":"2020-04-06T21:07:55.202827","indexId":"70206881","displayToPublicDate":"2019-11-22T06:58:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","docAbstract":"The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2019.1690780","usgsCitation":"Gumma, M.K., Thenkabail, P., Pardhasaradhi Teluguntla, and Oliphant, A., 2020, Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud: GIScience and Remote Sensing, v. 57, no. 3, p. 302-322, https://doi.org/10.1080/15481603.2019.1690780.","productDescription":"21 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Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gumma, Murali Krishna","contributorId":127590,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":776137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pardhasaradhi Teluguntla 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":214457,"corporation":false,"usgs":false,"family":"Pardhasaradhi Teluguntla","affiliations":[{"id":39046,"text":"Bay Area Environmental Research Institute at USGS","active":true,"usgs":false}],"preferred":false,"id":776138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776139,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208084,"text":"70208084 - 2020 - Methylmercury exposure in wildlife: A review of the ecological and physiological processes affecting contaminant concentrations and their interpretation","interactions":[],"lastModifiedDate":"2020-01-27T20:01:40","indexId":"70208084","displayToPublicDate":"2019-11-21T20:01:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Methylmercury exposure in wildlife: A review of the ecological and physiological processes affecting contaminant concentrations and their interpretation","docAbstract":"Exposure to methylmercury (MeHg) can result in detrimental health effects in wildlife. With advances in ecological indicators and analytical techniques for measurement of MeHg in a variety of tissues, numerous processes have been identified that can influence MeHg concentrations in wildlife. This review presents a synthesis of theoretical principals and applied information for measuring MeHg exposure and interpreting MeHg concentrations in wildlife. Mercury concentrations in wildlife are the net result of ecological processes influencing dietary exposure combined with physiological processes that regulate assimilation, transformation, and elimination. Therefore, consideration of both physiological and ecological processes should be integrated when formulating biomonitoring strategies. Ecological indicators, particularly stable isotopes of carbon, nitrogen, and sulfur, compound-specific stable isotopes, and fatty acids, can be effective tools to evaluate dietary MeHg exposure. Animal species differ in their physiological capacity for MeHg elimination, and animal tissues can be inert or physiologically active, act as sites of storage, transformation, or excretion of MeHg, and vary in the timing of MeHg exposure they represent. Biological influences such as age, sex, maternal transfer, and growth or fasting are also relevant for interpretation of tissue MeHg concentrations. Wildlife tissues that represent current or near-term bioaccumulation and in which MeHg is the predominant mercury species (such as blood and eggs) are most effective for biomonitoring ecosystems and understanding landscape drivers of MeHg exposure. Further research is suggested to critically evaluate the use of keratinized external tissues to measure MeHg bioaccumulation, particularly for less-well studied wildlife such as reptiles and terrestrial mammals. Suggested methods are provided to effectively use wildlife for quantifying patterns and drivers of MeHg bioaccumulation over time and space, as well as for assessing the potential risk and toxicological effects of MeHg on wildlife.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.135117","usgsCitation":"Chetelat, J., Ackerman, J., Eagles-Smith, C., and Hebert, C.E., 2020, Methylmercury exposure in wildlife: A review of the ecological and physiological processes affecting contaminant concentrations and their interpretation: Science of the Total Environment, v. 711, 135117, https://doi.org/10.1016/j.scitotenv.2019.135117.","productDescription":"135117","ipdsId":"IP-112784","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458469,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.135117","text":"Publisher Index Page"},{"id":371629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"711","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chetelat, John","contributorId":221830,"corporation":false,"usgs":false,"family":"Chetelat","given":"John","email":"","affiliations":[{"id":40438,"text":"Environment and Climate Change Canada, National Wildlife Research Centre","active":true,"usgs":false}],"preferred":false,"id":780416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":780415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":780417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hebert, Craig E.","contributorId":127337,"corporation":false,"usgs":false,"family":"Hebert","given":"Craig","email":"","middleInitial":"E.","affiliations":[{"id":6781,"text":"Environment Canada, Carelton University, Ottawa, Canada","active":true,"usgs":false}],"preferred":false,"id":780418,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230128,"text":"70230128 - 2020 - Pleistocene lakes and paleohydrologic environments of the Tecopa basin, California: Constraints on the drainage integration of the Amargosa River","interactions":[],"lastModifiedDate":"2022-03-30T16:07:49.15533","indexId":"70230128","displayToPublicDate":"2019-11-21T11:02:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Pleistocene lakes and paleohydrologic environments of the Tecopa basin, California: Constraints on the drainage integration of the Amargosa River","docAbstract":"The Tecopa basin in eastern California was a terminal basin that episodically held lakes during most of the Quaternary until the basin and its modern stream, the Amargosa River, became tributary to Death Valley. Although long studied for its sedimentology, diagenesis, and paleomagnetism, the basin’s lacustrine and paleoclimate history has not been well understood, and conflicting interpretations exist concerning the relations of Tecopa basin to the Amargosa River and to pluvial Lake Manly in Death Valley. Previous studies also did not recognize basinwide tectonic effects on lake-level history. In this study, we focused on: (1) establishing a chronology of shoreline deposits, as the primary indicator of lake-level history, utilizing well-known ash beds and new uranium-series and luminescence dating; (2) using ostracodes as indicators of water chemistry and water source(s); and (3) correlating lake transgressions to well-preserved fluvial-deltaic sequences. During the early Pleistocene, the Tecopa basin hosted small shallow lakes primarily fed by low-alkalinity water sourced mainly from runoff and (or) a groundwater source chemically unlike the modern springs. The first lake that filled the basin occurred just prior and up to the eruption of the 765 ka Bishop ash during marine isotope stage (MIS) 19; this lake heralded the arrival of the Amargosa River, delivering high-alkalinity water. Two subsequent lake cycles, coeval with MIS 16 (leading up to eruption of 631 ka Lava Creek B ash) and MIS 14 and (or) MIS 12, are marked by prominent accumulations of nearshore and beach deposits. The timing of the youngest of these three lakes, the High lake, is constrained by a uranium-series age of ca. 580 ± 120 ka on tufa-cemented beach gravel and by estimates from sedimentation rates. Highstand deposits of the Lava Creek and High lakes at the north end of the basin are stratigraphically tied to distinct sequences of fluvial-deltaic deposits fed by alkaline waters of the Amargosa River. The High lake reached the highest level achieved in the Tecopa basin, and it may have briefly discharged southward but did not significantly erode its threshold. The High lake was followed by a long hiatus of as much as 300 k.y., during which there is evidence for alluvial, eolian, and groundwater-discharge deposition, but no lakes. We attribute this hiatus, as have others, to blockage of the Amargosa River by an alluvial fan upstream near Eagle Mountain. A final lake, the Terminal lake, formed when the river once again flowed south into Tecopa basin, but it was likely short-lived due to rapid incision of the former threshold south of Tecopa. Deposits of the Terminal lake are inset below, and are locally unconformable on, deposits of the High lake and the nonlacustrine deposits of the hiatus. The Terminal lake reached its highstand at ca. 185 ± 21 ka, as dated by infrared-stimulated luminescence on feldspar in beach sand, a time coincident with perennial lake mud and alkaline-tolerant ostracodes in the Badwater core of Lake Manly during MIS 6. A period of stillstand occurred as the Terminal lake drained when the incising river encountered resistant Stirling Quartzite near the head of present-day Amargosa Canyon. Our studies significantly revise the lacustrine and drainage history of the Tecopa basin, show that the MIS 6 highstand was not the largest lake in the basin as previously published (with implications for potential nuclear waste storage at Yucca Mountain, Nevada), and provide evidence from shoreline elevations for ∼20 m of tectonic uplift in the northern part of the basin across an ENE-trending monoclinal flexure.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35282.1","usgsCitation":"Reheis, M.C., Caskey, J., Bright, J., Paces, J.B., Mahan, S.A., and Wan, E., 2020, Pleistocene lakes and paleohydrologic environments of the Tecopa basin, California: Constraints on the drainage integration of the Amargosa River: GSA Bulletin, v. 132, no. 7-8, p. 1537-1565, https://doi.org/10.1130/B35282.1.","productDescription":"29 p.","startPage":"1537","endPage":"1565","ipdsId":"IP-105957","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":397866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Tecopa basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.25,\n 35\n ],\n [\n -115.5,\n 35\n ],\n [\n -115.5,\n 37\n ],\n [\n -117.25,\n 37\n ],\n [\n -117.25,\n 35\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Reheis, Marith C. 0000-0002-8359-323X mreheis@usgs.gov","orcid":"https://orcid.org/0000-0002-8359-323X","contributorId":138571,"corporation":false,"usgs":true,"family":"Reheis","given":"Marith","email":"mreheis@usgs.gov","middleInitial":"C.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caskey, John","contributorId":289506,"corporation":false,"usgs":false,"family":"Caskey","given":"John","email":"","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":839196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bright, Jordon","contributorId":63981,"corporation":false,"usgs":false,"family":"Bright","given":"Jordon","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":839197,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839199,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":3434,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":839200,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211346,"text":"70211346 - 2020 - Do the quality and quantity of honey bee-collected pollen vary across an agricultural land use gradient?","interactions":[],"lastModifiedDate":"2020-07-27T15:40:11.361339","indexId":"70211346","displayToPublicDate":"2019-11-21T10:36:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1536,"text":"Environmental Entomology","active":true,"publicationSubtype":{"id":10}},"title":"Do the quality and quantity of honey bee-collected pollen vary across an agricultural land use gradient?","docAbstract":"Pollen is the source of protein for most bee species, yet the quality and quantity of pollen is variable across landscapes and growing seasons. Understanding the role of landscapes in providing nutritious forage to bees is important for pollinator health, particularly in areas undergoing significant land-use change such as in the Northern Great Plains (NGP) region of the United States where grasslands are being converted to row crops. We investigated how the quality and quantity of pollen collected by honey bees (Apis mellifera L. [Hymenoptera: Apidae]) changed with land use and across the growing season by sampling bee-collected pollen from apiaries in North Dakota, South Dakota, and Minnesota, USA, throughout the flowering season in 2015–2016. We quantified protein content and quantity of pollen to investigate how they varied temporally and across a land-use gradient of grasslands to row crops. Neither pollen weight nor crude protein content varied linearly across the land-use gradient; however, there were significant interactions between land use and sampling date across the season, particularly in grasslands. Generally, pollen protein peaked mid-July while pollen weight had two maxima in late-June and late-August. Results suggest that while land use itself may not correlate with the quality or quantity of pollen resources collected by honey bees among our study apiaries, the nutritional landscape of the NGP is seasonally dynamic, especially in certain land covers, and may impose seasonal resource limitations for both managed and native bee species. Furthermore, results indicate periods of qualitative and quantitative pollen dearth may not coincide.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/ee/nvz139","usgsCitation":"Simanonok, M., Otto, C., and Smart, M.D., 2020, Do the quality and quantity of honey bee-collected pollen vary across an agricultural land use gradient?: Environmental Entomology, v. 49, no. 1, p. 189-196, https://doi.org/10.1093/ee/nvz139.","productDescription":"8 p.","startPage":"189","endPage":"196","ipdsId":"IP-110130","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458470,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvz139","text":"Publisher Index Page"},{"id":437197,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DKMY4L","text":"USGS data release","linkHelpText":"Data release for: Does the quality and quantity of honey bee-collected pollen vary across an agricultural land-use gradient?"},{"id":376721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.2744140625,\n 43.54854811091286\n ],\n [\n -91.51611328125,\n 44.02442151965934\n ],\n [\n -93.0322265625,\n 44.85586880735725\n ],\n [\n -92.79052734375,\n 45.42929873257377\n ],\n [\n -92.92236328125,\n 45.644768217751924\n ],\n [\n -92.35107421874999,\n 46.10370875598026\n ],\n [\n -92.197265625,\n 46.81509864599243\n ],\n [\n -89.49462890625,\n 48.09275716032736\n ],\n [\n -90.98876953125,\n 48.268569112964336\n ],\n [\n -92.79052734375,\n 48.58932584966975\n ],\n [\n -93.84521484375,\n 48.516604348867475\n ],\n [\n -94.59228515625,\n 48.80686346108517\n ],\n [\n -94.8779296875,\n 49.396675075193976\n ],\n [\n -95.25146484374999,\n 49.396675075193976\n ],\n [\n -95.16357421875,\n 48.951366470947725\n ],\n [\n -104.12841796875,\n 49.023461463214126\n ],\n [\n -104.08447265624999,\n 43.03677585761058\n ],\n [\n -98.6572265625,\n 43.03677585761058\n ],\n [\n -97.91015624999999,\n 42.74701217318067\n ],\n [\n -96.85546875,\n 42.779275360241904\n ],\n [\n -96.30615234375,\n 42.439674178149424\n ],\n [\n -96.5478515625,\n 43.54854811091286\n ],\n [\n -91.2744140625,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Simanonok, Michael P. 0000-0002-4710-4515","orcid":"https://orcid.org/0000-0002-4710-4515","contributorId":229685,"corporation":false,"usgs":true,"family":"Simanonok","given":"Michael P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":793952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":793953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smart, Matthew D.","contributorId":229686,"corporation":false,"usgs":false,"family":"Smart","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":16587,"text":"University of Nebraska Lincoln","active":true,"usgs":false}],"preferred":false,"id":793954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208919,"text":"70208919 - 2020 - The ecology of chronic wasting disease in wildlife","interactions":[],"lastModifiedDate":"2020-03-05T10:26:47","indexId":"70208919","displayToPublicDate":"2019-11-21T10:26:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"The ecology of chronic wasting disease in wildlife","docAbstract":"Prions are misfolded infectious proteins responsible for a group of fatal neurodegenerative diseases termed transmissible spongiform encephalopathy or prion diseases. Chronic Wasting Disease (CWD) is the prion disease with the highest spillover potential, affecting at least seven Cervidae (deer) species. The zoonotic potential of CWD is inconclusive and cannot be ruled out. A risk of infection for other domestic and wildlife species is also plausible. Here, we review the current status of the knowledge with respect to CWD ecology in wildlife. Our current understanding of the geographic distribution of CWD lacks spatial and temporal detail, does not consider the biogeography of infectious diseases, and is largely biased by sampling based on hunters' cooperation and funding available for each region. Limitations of the methods used for data collection suggest that the extent and prevalence of CWD in wildlife is underestimated. If the zoonotic potential of CWD is confirmed in the short term, as suggested by recent results obtained in experimental animal models, there will be limited accurate epidemiological data to inform public health. Research gaps in CWD prion ecology include the need to identify specific biological characteristics of potential CWD reservoir species that better explain susceptibility to spillover, landscape and climate configurations that are suitable for CWD transmission, and the magnitude of sampling bias in our current understanding of CWD distribution and risk. Addressing these research gaps will help anticipate novel areas and species where CWD spillover is expected, which will inform control strategies. From an ecological perspective, control strategies could include assessing restoration of natural predators of CWD reservoirs, ultrasensitive CWD detection in biotic and abiotic reservoirs, and deer density and landscape modification to reduce CWD spread and prevalence.
","language":"English","publisher":"Wiley","doi":"10.1111/brv.12568","usgsCitation":"Escobar, L.E., Pritzkow, S., Winter, S.N., Grear, D.A., Kirchgessner, M.S., Dominguez-Villegas, E., Machado, G., Peterson, A.T., and Soto, C., 2020, The ecology of chronic wasting disease in wildlife: Biological Reviews, v. 95, no. 2, p. 393-408, https://doi.org/10.1111/brv.12568.","productDescription":"16 p.","startPage":"393","endPage":"408","ipdsId":"IP-107301","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":458473,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/brv.12568","text":"External Repository"},{"id":372946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Finland, Norway, South Korea, Sweden, United States","volume":"95","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Escobar, Luis E.","contributorId":178962,"corporation":false,"usgs":false,"family":"Escobar","given":"Luis","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":784013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pritzkow, Sandra","contributorId":223075,"corporation":false,"usgs":false,"family":"Pritzkow","given":"Sandra","email":"","affiliations":[{"id":40666,"text":"University of Texas Medical School at Houston","active":true,"usgs":false}],"preferred":false,"id":784014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winter, Steven N","contributorId":223076,"corporation":false,"usgs":false,"family":"Winter","given":"Steven","email":"","middleInitial":"N","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":784015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":784012,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirchgessner, Megan S.","contributorId":173866,"corporation":false,"usgs":false,"family":"Kirchgessner","given":"Megan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":784016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dominguez-Villegas, Ernesto","contributorId":223077,"corporation":false,"usgs":false,"family":"Dominguez-Villegas","given":"Ernesto","email":"","affiliations":[{"id":37079,"text":"Wildlife Center of Virginia","active":true,"usgs":false}],"preferred":false,"id":784017,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Machado, Gustavo","contributorId":223078,"corporation":false,"usgs":false,"family":"Machado","given":"Gustavo","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":784018,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peterson, A Townsend","contributorId":223079,"corporation":false,"usgs":false,"family":"Peterson","given":"A","email":"","middleInitial":"Townsend","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":784019,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Soto, Claudio","contributorId":223080,"corporation":false,"usgs":false,"family":"Soto","given":"Claudio","email":"","affiliations":[{"id":40666,"text":"University of Texas Medical School at Houston","active":true,"usgs":false}],"preferred":false,"id":784020,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223511,"text":"70223511 - 2020 - Nonlinear patterns in mercury bioaccumulation in American alligators are a function of predicted age","interactions":[],"lastModifiedDate":"2021-08-31T13:00:27.640555","indexId":"70223511","displayToPublicDate":"2019-11-21T07:53:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Nonlinear patterns in mercury bioaccumulation in American alligators are a function of predicted age","docAbstract":"Mercury is a widespread, naturally occurring contaminant that biomagnifies in wetlands due to the methylation of this element by sulfate-reducing bacteria. Species that feed at the top trophic level within wetlands are predicted to have higher mercury loads compared to species feeding at lower trophic levels and are therefore often used for mercury biomonitoring. However, mechanisms for mercury bioaccumulation in sentinel species are often poorly understood, due to a lack of long-term studies or an inability to differentiate between confounding variables. We examined mercury bioaccumulation patterns in the whole blood of American alligators (Alligator mississippiensis) from a long-term mark-recapture study (1979–2017) in South Carolina, USA. Using a growth model and auxiliary information on predicted age at first capture, we differentiated between age- and size-related variation in mercury bioaccumulation, which are often confounded in alligators due to their determinate growth pattern. Contrary to predictions that the oldest or largest individuals were likely to have the highest mercury concentrations, our best-supported model indicated a peak in mercury concentration at 30–40 years of age, depending on the sex, and lower concentrations in the youngest and oldest animals. To evaluate the robustness of our findings, we re-analyzed data from a previously published study of mercury in alligators sampled at Merritt Island National Wildlife Refuge in Florida. Unlike the South Carolina data, the data from Florida contained minimal auxiliary information regarding age, yet the best supported model similarly indicated a peaked rather than increasing relationship between mercury and body size, a less-precise indicator of age. These findings highlight how long-term monitoring can differentiate between confounding variables (e.g., age and size) to better elucidate complex relationships between contaminant exposure and demographic factors in sentinel species.
We measure pseudospectral and peak ground motions from 44 intermediate‐depth
The 30 November 2018
Monitoring population performance in the years following species reintroductions is key to assessing population restoration success and evaluating assumptions made in planning species restoration programs. From 2008–2010 we translocated 90 fishers (Pekania pennanti) from British Columbia, Canada, to Washington's Olympic Peninsula, USA, providing the opportunity to evaluate modeling assumptions used to identify the most suitable reintroduction areas in Washington and enhance understanding of fisher habitat associations in the late‐successional forest ecosystems in the coastal Pacific Northwest. From 2013–2016, we deployed 788 motion‐sensing cameras and hair (DNA)‐snaring devices distributed among 263 24‐km2 primary sampling units across the Olympic Peninsula. Our objectives were to determine whether occupancy patterns of the reestablishing population supported assumptions of the initial habitat assessment models, whether the population had expanded or shifted in distribution since the initial reintroductions, compare physical habitat attributes among land‐management designations, and determine whether the founding fishers had successfully reproduced. We predicted that site occupancy by fishers would be associated with landscapes characterized by high proportional coverage of dense forest canopies and medium‐sized and large trees, a diversity of stand structural classes, and area near the administrative boundary separating wilderness from more intensively managed forest lands. We detected fishers across designated wilderness, federal lands outside of wilderness, and other land designations in proportion to land availability on the Peninsula. We found negligible support for predictions that occupancy by fishers was associated with percent forest cover, tree‐size class, or structural class diversity. Rather, occupancy was strongly associated with lands near the wilderness boundary on both sides. We speculate that the boundary between wilderness and more intensively managed forest lands provided fishers with the most suitable prey in proximity to contiguous expanses of low‐ to mid‐elevation late‐successional forests that provided optimal resting, denning, and security values. Occupancy patterns shifted toward the west and south along a precipitation gradient during the study, indicating that population distribution had not yet stabilized 5–8 years following translocation. Genetic results indicated that ≥2 generations of fishers have been produced on the Peninsula. Annual occupancy rates across the Peninsula (0.08–0.24) were lower than in other previously studied and established fisher populations, indicating that not all habitat was fully occupied or that initial estimates of the extent of habitat was overestimated. The strong selection fishers exhibited for wilderness edge and weak selection against extensive forested wilderness areas suggested that habitat managers should strive for maintaining a suitable interspersion of required forest structures and biotic habitat components, such as prey resource availability.
Fog and low cloud cover (FLCC) and late summer recharge increase stream baseflow and decrease stream temperature during arid Mediterranean climate summers, which benefits salmon especially under climate warming conditions. The potential to discharge cool water to streams during the late summer (hydrologic capacity; HC) furnished by FLCC and recharge were mapped for the 299 subwatersheds ranked Core, Phase 1, or Phase 2 under the National Marine Fisheries Service Recovery Plan that prioritized restoration and threat abatement action for endangered Central California Coast Coho Salmon evolutionarily significant unit. Two spatially continuous gridded datasets were merged to compare HC: average hrs/day FLCC, a new dataset derived from a decade of hourly National Weather Satellite data, and annual average mm recharge from the USGS Basin Characterization Model. Two use‐case scenarios provide examples of incorporating FLCC‐driven HC indices into long‐term recovery planning. The first, a thermal analysis under future climate, projected 65% of the watershed area for 8–19 coho population units as thermally inhospitable under two global climate models and identified several units with high resilience (high HC under the range of projected warming conditions). The second use case investigated HC by subwatershed rank and coho population, and identified three population units with high HC in areas ranked Phase 1 and 2 and low HC in Core. Recovery planning for cold‐water fish species would benefit by including FLCC in vulnerability analyses.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12811","usgsCitation":"Torregrosa, A.A., Flint, L.E., and Flint, A.L., 2020, Hydrologic resilience from summertime fog and recharge: A case study for coho salmon recovery planning: Journal of the American Water Resources Association, v. 56, no. 1, p. 134-160, https://doi.org/10.1111/1752-1688.12811.","productDescription":"27 p.","startPage":"134","endPage":"160","ipdsId":"IP-095384","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458480,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12811","text":"Publisher Index Page"},{"id":372761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.18945312500001,\n 41.96765920367816\n ],\n [\n -128.0126953125,\n 38.39333888832238\n ],\n [\n -122.9150390625,\n 34.08906131584994\n ],\n [\n -117.79541015625001,\n 36.82687474287728\n ],\n [\n -124.18945312500001,\n 41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":783455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":783457,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213237,"text":"70213237 - 2020 - Inoculation and habitat amelioration efforts in biological soil crust recovery vary by desert and soil texture","interactions":[],"lastModifiedDate":"2020-09-16T01:09:05.042409","indexId":"70213237","displayToPublicDate":"2019-11-20T08:27:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Inoculation and habitat amelioration efforts in biological soil crust recovery vary by desert and soil texture","docAbstract":"As dryland degradation continues, it is increasingly important to understand how to effectively restore biocrust communities. Potential techniques include the addition of biocrust inoculum to accelerate biocrust recovery. Enhanced erosion typical of degraded environments creates a challenge for these approaches, due to loss by wind or water and burial by saltating particles. To retain and protect added inoculum, the inclusion of habitat‐amelioration techniques can improve recovery rates. This study tested three different types of inoculum (field‐collected, greenhouse‐cultivated, and laboratory‐cultivated biocrust) coupled with two treatments to augment soil stability and ameliorate habitat limitations: soil surface polyacrylamide additions and installation of straw barriers. This was done across two deserts (Great Basin and Chihuahuan) and separated into generally coarse‐ or finer‐textured soils in each desert, with results monitored for 3 years (2015, 2016, 2017). While the inoculum type, coupled with habitat ameliorations, occasionally enhanced biocrust growth across years and treatments, in other cases, it made no difference compared to natural recovery rates. Rather, the desert location and soil texture groupings were the most prominent factors in determining recovery trajectories. Recovery proportions were similar in the finer‐textured sites in both the Great Basin and the Chihuahuan deserts, while the coarser‐textured site in the Great Basin did show some recovery over time and the Chihuahuan coarser‐textured site did not. This study demonstrates the importance of understanding site potential and identifying key limitations to biocrust recovery for successful restoration projects.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13087","usgsCitation":"Faist, A.M., Antoninka, A.J., Belnap, J., Bowker, M.A., Duniway, M.C., Garcia-Pichel, F., Nelson, C., Reed, S.C., Giraldo Silva, A., Velasco-Ayuso, S., and Barger, N.N., 2020, Inoculation and habitat amelioration efforts in biological soil crust recovery vary by desert and soil texture: Restoration Ecology, v. 28, no. S2, p. s96-s105, https://doi.org/10.1111/rec.13087.","productDescription":"10 p.","startPage":"s96","endPage":"s105","ipdsId":"IP-108283","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":378394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Southern New Mexico, Western Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.962890625,\n 32.509761735919426\n ],\n [\n -105.46875,\n 31.466153715024294\n ],\n [\n -102.26074218749999,\n 31.42866311735861\n ],\n [\n -101.162109375,\n 32.21280106801518\n ],\n [\n -101.337890625,\n 33.063924198120645\n ],\n [\n -106.962890625,\n 32.509761735919426\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"S2","noUsgsAuthors":false,"publicationDate":"2020-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Faist, Akasha M.","contributorId":193038,"corporation":false,"usgs":false,"family":"Faist","given":"Akasha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":798695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antoninka, Anita J.","contributorId":216042,"corporation":false,"usgs":false,"family":"Antoninka","given":"Anita","email":"","middleInitial":"J.","affiliations":[{"id":39356,"text":"School of Forestry, Northern Arizona University, Flagstaff, AZ, 86011, USA","active":true,"usgs":false}],"preferred":false,"id":798696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowker, Matthew A. mbowker@usgs.gov","contributorId":2875,"corporation":false,"usgs":true,"family":"Bowker","given":"Matthew","email":"mbowker@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":798697,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798671,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garcia-Pichel, Ferran","contributorId":166779,"corporation":false,"usgs":false,"family":"Garcia-Pichel","given":"Ferran","email":"","affiliations":[{"id":24511,"text":"Arizona State University, Tempe AZ USA 85287","active":true,"usgs":false}],"preferred":false,"id":798698,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, Corey","contributorId":240676,"corporation":false,"usgs":false,"family":"Nelson","given":"Corey","email":"","affiliations":[],"preferred":false,"id":798699,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798700,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giraldo Silva, Ana","contributorId":181758,"corporation":false,"usgs":false,"family":"Giraldo Silva","given":"Ana","email":"","affiliations":[],"preferred":false,"id":798701,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Velasco-Ayuso, Sergio","contributorId":240677,"corporation":false,"usgs":false,"family":"Velasco-Ayuso","given":"Sergio","email":"","affiliations":[],"preferred":false,"id":798702,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Barger, Nichole N.","contributorId":193039,"corporation":false,"usgs":false,"family":"Barger","given":"Nichole","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":798703,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70216943,"text":"70216943 - 2020 - Could a freshwater fish be at the root of dystrophic crises in a coastal lagoon?","interactions":[],"lastModifiedDate":"2020-12-17T14:06:31.950476","indexId":"70216943","displayToPublicDate":"2019-11-20T07:52:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Could a freshwater fish be at the root of dystrophic crises in a coastal lagoon?","docAbstract":"Eutrophication has a profound impact on ecosystems worldwide. Grass carp Ctenopharyngodon idella, an herbivorous fish, has been introduced to control aquatic plant overgrowth caused by eutrophication, but could have other, potentially detrimental, effects. We used the Po di Volano basin (south of the Po River delta, northern Italy) as a test case to explore whether grass carp effects on canal aquatic vegetation could be at the root of historical changes in N loads exported from the basin to the Goro Lagoon. We modeled the aquatic vegetation production and standing crop, its denitrification potential, and its consumption by introduced grass carp. We then examined whether changes in historical nitrogen loads matched the modeled losses of the drainage network denitrification function or other changes in agricultural practices. Our results indicate that introduced grass carp could completely remove submerged vegetation in the Po di Volano canal network, which could – in turn – lead to substantial loss of the denitrification function of the system, causing in an increase in downstream nitrogen loads. A corresponding increase, matching both timing and magnitude, was detected in historical nitrogen loads to the Goro Lagoon, which were significantly different before and after the time of modeled collapse of the denitrification function. This increase was not clearly linked to watershed use or agricultural practices, which implies that the loss of the denitrification function through grass carp overgrazing could be a likely explanation of the increase in downstream nitrogen loads. Perhaps for the first time, we provide evidence that a freshwater fish introduction could have caused long-lasting changes in nutrient dynamics that are exported downstream to areas where the fish is not present.
We use >10,000 km of high-resolution seismic-reflection data together with multibeam bathymetry to document complex and highly variable post-Last Glacial Maximum (LGM) sediment distribution and thickness in the coastal zone (~10 m isobath to 5.6 km offshore) along a ~800 km section of central California's transform continental margin. Sediment thickness ranges from 0 (seafloor bedrock) to 64 m with a mean of 8.7 m. We delineate 25 coastal zone “sediment domains,” and group them based on common geomorphology and sediment occurrence. Thickest sediment occurs in “mountain front” and “large river” domains, which comprise 14.5% and 7.9% of the coastal zone and contain 30.1% and 18.2% of coastal zone sediment, respectively. In contrast, “small river” domains and “sediment-poor shelf” domains comprise 50.7% and 15.7% of the coastal zone and contain 18.4% and 12.7% of its sediment.
The distribution and thickness of post-LGM sediment in the coastal zone is controlled by a combination of tectonics, sediment supply, and eustasy. Sediment is derived from a tectonically controlled coastal landscape of rapidly uplifting mountain fronts, more slowly uplifting marine terraces, and fault-bounded headlands and alluvial-estuarine troughs. Sediment supply is maximized along steep, landslide-prone, mountain fronts and at the mouths of large watersheds, and minimized along lower-relief, terraced coastal landscape drained by smaller rivers and creeks. In the offshore coastal zone, tectonics generates local uplifts and basins, and influences shelf width and gradient as well as the locations of some shelf-incised submarine canyons. Sea-level rise raises base level, drowns estuaries, creates accommodation space on the shelf (amount based on gradient), and isolates the heads of many submarine canyons at or near the shelfbreak. Comparison of shelf sediment volumes with estimates of “unaltered” watershed sediment supply reveals that a relatively small proportion of post-LGM sediment supply is preserved on the shelf offshore of some of the largest rivers. Sediments deposited in shoreline and shelf environments have limited preservation potential, and the most complete long-term geologic record of the post-LGM transgression and highstand is likely represented in slope and submarine fan deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2019.106085","usgsCitation":"Johnson, S., Beeson, J.W., Watt, J., Sliter, R., and Papesh, A., 2020, Controls on sediment distribution in the coastal zone of the central California transform continental margin, USA: Marine Geology, v. 420, 106085, 29 p., https://doi.org/10.1016/j.margeo.2019.106085.","productDescription":"106085, 29 p.","ipdsId":"IP-110134","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2019.106085","text":"Publisher Index Page"},{"id":370327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.93652343749999,\n 33.43144133557529\n ],\n [\n -119.794921875,\n 33.43144133557529\n ],\n [\n -119.794921875,\n 40.212440718286466\n ],\n [\n -124.93652343749999,\n 40.212440718286466\n ],\n [\n -124.93652343749999,\n 33.43144133557529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"420","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":221270,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":777608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beeson, Jeffrey W. 0000-0002-7396-237X","orcid":"https://orcid.org/0000-0002-7396-237X","contributorId":194964,"corporation":false,"usgs":false,"family":"Beeson","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":777609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watt, Janet 0000-0002-4759-3814","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":221271,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":777610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sliter, Ray 0000-0003-0337-3454","orcid":"https://orcid.org/0000-0003-0337-3454","contributorId":221272,"corporation":false,"usgs":true,"family":"Sliter","given":"Ray","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":777611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Papesh, Antoinette 0000-0002-1704-0557","orcid":"https://orcid.org/0000-0002-1704-0557","contributorId":221273,"corporation":false,"usgs":false,"family":"Papesh","given":"Antoinette","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":777612,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207435,"text":"70207435 - 2020 - Relaxing segmentation on the Wasatch Fault Zone: Impact on seismic hazard ","interactions":[],"lastModifiedDate":"2020-02-06T11:12:48","indexId":"70207435","displayToPublicDate":"2019-11-19T13:23:48","publicationYear":"2020","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":"Relaxing segmentation on the Wasatch Fault Zone: Impact on seismic hazard ","docAbstract":"The multisegment Wasatch fault zone is a well-studied normal fault in the western United States that has paleoseismic evidence of recurrent Holocene surface-faulting earthquakes. Along the 270-km-long central part of the fault, four primary structural complexities provide possible along-strike limits to these ruptures and form the basis for models of fault segmentation. Here, we assess the impact that the Wasatch fault segmentation model has on seismic hazard by evaluating the time-independent long-term rate of ruptures on the fault that satisfy fault slip rates and paleoseismic event rates, adapting standard inverse theory used in the Uniform California Earthquake Rupture Forecast 3 (UCERF3), and implementing a segmentation constraint where ruptures across primary structural complexities are penalized. We define three models with varying degrees of rupture penalization: (1) segmented (ruptures confined to individual segments), (2) penalized (multi-segment ruptures allowed, but penalized), and (3) unsegmented (all ruptures allowed). Seismic-hazard results show that on average, hazard is highest for the segmented model, where seismic moment is accommodated by frequent moderate (moment magnitude, Mw 6.2–6.8) earthquakes. The unsegmented model yields the lowest average seismic hazard because part of the seismic moment is accommodated by large (Mw 6.9–7.9), but infrequent ruptures. We compare these results to model differences derived from other inputs such as slip rate and magnitude scaling relationships and conclude that segmentation exerts a primary control on seismic hazard. This study demonstrates the need for additional geologic constraints on rupture extent and methods by which these observations can be included in hazard-modeling efforts.
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governance","docAbstract":"Despite increasing scientific understanding of the global environmental crisis, we struggle to adopt the policies and practices science suggests we should. One of the reasons for this is the general absence of inclusive engagement and dialogue among a wide range of actors with distinct interactions with nature. Furthermore, there is little consideration of the role of language in understanding and shaping human-nature relations across different worldviews and cultures. In this paper, we propose that engagement and dialogue between the different actors involved in, or affected by, efforts to address the global environmental crisis can be strengthened by being mindful of the breadth of the diverse human-nature relations found around the globe. Examininge diverse conceptualizations of “nature” in more than 60 languages, we find that concpetualisaitions of nature fall into three broad clusters: inclusive conceptualizations where humans are viewed as an integral component of nature; non-inclusive conceptualizations where humans are separate from nature; and deifying conceptualizations where nature is understood and experienced within a spiritual dimension. \nConsidering and respecting this rich repertoire for describing, thinking about and relating to nature can help us articulating global environmental governance in ways that resonate across cultures and worldviews. This repertoire also provides a resource we can draw on when defining policies, sustainability scenarios and practical interventions for the future thus offering opportunities for finding solutions to global environmental challenges, such as illustrated by the different laws granting legal personality to nature adopted around the world.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsci.2019.10.018","usgsCitation":"Coscieme, L., da Silva Hyldmo, H., Fernandez-Llamazares, A., Palomo, I., Mwampamba, T.H., Selomane, O., Sitas, N., Jaureguiberry, P., Takahashi, Y., Lim, M., Barral, M.P., Farinaci, J.S., Diaz-Jose, J., Ghosh, S., Ojino, J., Alassaf, A., Baatuuwie, B.N., Balint, L., Basher, Z., Boeraeve, F., Budiharta, S., Chen, R., Desrousseaux, M., Dowo, G., Febria, C.M., Ghazi, H., Harmackova, Z.V., Jaffe, R., Kalemba, M.M., Lambini, C.K., Lasmana, F.P., Mohammed, A.A., Niamir, A., Pliscoff, P., Sabyrbekov, R., Sidorovich, A.A., Thompson, L., Shrestha, U.B., and Valle, M., 2020, Multiple conceptualizations of nature are key to inclusivity and legitimacy in global environmental governance: Environmental Science and Policy, v. 104, p. 36-42, https://doi.org/10.1016/j.envsci.2019.10.018.","productDescription":"7 p.","startPage":"36","endPage":"42","ipdsId":"IP-113461","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":458492,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.envsci.2019.10.018","text":"External Repository"},{"id":370678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coscieme, Luca","contributorId":221499,"corporation":false,"usgs":false,"family":"Coscieme","given":"Luca","email":"","affiliations":[{"id":40395,"text":"School of Natural Sciences, Dept. 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By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose (Anser brachyrhynchus) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 %), a negative bias in capture-mark-recapture estimates of survival (-3 %), and a negative bias in indices of productivity (-23 %). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2019.108869","usgsCitation":"Johnson, F., Zimmerman, G.S., Jensen, G.H., Clausen, K.K., Frederiksen, M., and Madsen, J., 2020, Using integrated population models for insights into monitoring programs: An application using pink-footed geese: Ecological Modelling, v. 415, 108869, 13 p., https://doi.org/10.1016/j.ecolmodel.2019.108869.","productDescription":"108869, 13 p.","ipdsId":"IP-107877","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437202,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P901K3RP","text":"USGS data release","linkHelpText":"Demographic parameters for Svalbard pink-footed geese, 1991-2018"},{"id":369802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"415","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Fred 0000-0002-5854-3695","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":220964,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":776392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":776393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jensen, Gitte H.","contributorId":220965,"corporation":false,"usgs":false,"family":"Jensen","given":"Gitte","email":"","middleInitial":"H.","affiliations":[{"id":13685,"text":"Aarhus University, Department of Bioscience","active":true,"usgs":false}],"preferred":false,"id":776394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clausen, Kevin K.","contributorId":174355,"corporation":false,"usgs":false,"family":"Clausen","given":"Kevin","email":"","middleInitial":"K.","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":776395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frederiksen, Morten","contributorId":217509,"corporation":false,"usgs":false,"family":"Frederiksen","given":"Morten","email":"","affiliations":[{"id":13685,"text":"Aarhus University, Department of Bioscience","active":true,"usgs":false}],"preferred":false,"id":776396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":776397,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228363,"text":"70228363 - 2020 - Quantifying contributions to tournament catches among resident, stocked, and hybrid black basses (Micropterus spp.)","interactions":[],"lastModifiedDate":"2022-02-10T12:18:04.089294","indexId":"70228363","displayToPublicDate":"2019-11-19T10:49:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Quantifying contributions to tournament catches among resident, stocked, and hybrid black basses (Micropterus spp.)","title":"Quantifying contributions to tournament catches among resident, stocked, and hybrid black basses (Micropterus spp.)","docAbstract":"Millions of Florida bass, Micropterus floridanus Lesueur, are stocked annually into populations of largemouth bass, Micropterus salmoides Lacepède, to increase trophy fish abundance. However, little effort has related the role that resultant hybrids make to angler catches. Largemouth bass were sampled from an important recreational fishery subject to extensive Florida bass stocking to address the hypothesis that anglers capture Florida bass, largemouth bass and hybrids at rates equivalent to their overall abundance in the population. Fin clips obtained from tournament angling events (n = 348) and boat-mounted electrofishing sampling (n = 219) were screened at 38 species-diagnostic markers and individuals were assigned to genealogical classes using Bayesian clustering algorithms. No significant differences were identified between angler and electrofishing catches providing evidence that hybridised individuals stemming from a long-term stocking programme may constitute an important contribution to tournament angling catch.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12403","usgsCitation":"Hargrove, J., Rogers, M.W., and Kacmar, P.T., 2020, Quantifying contributions to tournament catches among resident, stocked, and hybrid black basses (Micropterus spp.): Fisheries Management and Ecology, v. 27, p. 219-226, https://doi.org/10.1111/fme.12403.","productDescription":"8 p.","startPage":"219","endPage":"226","ipdsId":"IP-111011","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Chickamauga Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.2264404296875,\n 34.99625375979014\n ],\n [\n -84.94903564453125,\n 34.99625375979014\n ],\n [\n -84.94903564453125,\n 35.46738105960409\n ],\n [\n -85.2264404296875,\n 35.46738105960409\n ],\n [\n -85.2264404296875,\n 34.99625375979014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2019-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hargrove, John S.","contributorId":244463,"corporation":false,"usgs":false,"family":"Hargrove","given":"John S.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":833957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Mark W. 0000-0001-7205-5623","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":245525,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kacmar, Phillip T.","contributorId":275299,"corporation":false,"usgs":false,"family":"Kacmar","given":"Phillip","email":"","middleInitial":"T.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":833959,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227078,"text":"70227078 - 2020 - RAD-seq refines previous estimates of genetic structure in Lake Erie walleye","interactions":[],"lastModifiedDate":"2021-12-29T15:43:25.641325","indexId":"70227078","displayToPublicDate":"2019-11-19T09:36:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"RAD-seq refines previous estimates of genetic structure in Lake Erie walleye","docAbstract":"Delineating population structure helps fishery managers to maintain a diverse “portfolio” of local spawning populations (stocks), as well as facilitate stock-specific management. In Lake Erie, commercial and recreational fisheries for Walleye Sander vitreus exploit numerous local spawning populations, which cannot be easily differentiated using traditional genetic data (e.g., microsatellites). Here, we used genomic information (12,264 polymorphic loci) generated using restriction site-associated DNA sequencing to investigate stock structure in Lake Erie Walleye. We found low genetic divergence (genetic differentiation index FST = 0.0006–0.0019) among the four Lake Erie western basin stocks examined, which resulted in low classification accuracies for individual samples (40–60%). However, more structure existed between western and eastern Lake Erie basin stocks (FST = 0.0042–0.0064), resulting in greater than 95% classification accuracy of samples to a lake basin. Thus, our success in using genomics to identify stock structure varied with spatial scale. Based on our results, we offer suggestions to improve the efficacy of this new genetic tool for refining stock structure and eventually determining relative stock contributions in Lake Erie Walleye and other Great Lakes populations.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10215","usgsCitation":"Chen, K., Euclide, P., Ludsin, S., Larson, W., Sovic, M.G., Gibbs, H.L., and Marschall, E., 2020, RAD-seq refines previous estimates of genetic structure in Lake Erie walleye: Transactions of the American Fisheries Society, v. 149, no. 20, p. 159-173, https://doi.org/10.1002/tafs.10215.","productDescription":"15 p.","startPage":"159","endPage":"173","ipdsId":"IP-107069","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":393592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, New York, Ohio, Ontario, Pennsylvania","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.72802734375,\n 42.84375132629021\n ],\n [\n -81.34277343749999,\n 42.69858589169842\n ],\n [\n -83.21044921875,\n 42.5530802889558\n ],\n [\n -83.583984375,\n 42.01665183556825\n ],\n [\n -83.73779296875,\n 41.541477666790286\n ],\n [\n -82.63916015625,\n 41.16211393939692\n ],\n [\n -80.96923828125,\n 41.47566020027821\n ],\n [\n -79.716796875,\n 42.06560675405716\n ],\n [\n -78.72802734375,\n 42.84375132629021\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"20","noUsgsAuthors":false,"publicationDate":"2020-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Kuan-Yu","contributorId":270528,"corporation":false,"usgs":false,"family":"Chen","given":"Kuan-Yu","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Euclide, Peter T.","contributorId":270530,"corporation":false,"usgs":false,"family":"Euclide","given":"Peter T.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":829536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ludsin, Stuart A.","contributorId":270532,"corporation":false,"usgs":false,"family":"Ludsin","given":"Stuart A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":829534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sovic, Michael G.","contributorId":270534,"corporation":false,"usgs":false,"family":"Sovic","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829538,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibbs, H. 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However, it is difficult to test the relative contributions of migratory behaviour vs. other divergent traits to reproductive isolation. Comparing hybrid zones with and without migratory divides offers a rare opportunity to directly examine the contribution of divergent migratory behaviour to reproductive barriers. We show that across replicate sampling transects of two pairs of barn swallow (Hirundo rustica) subspecies, strong reproductive isolation coincided with a migratory divide spanning 20 degrees of latitude. A third subspecies pair exhibited no evidence for a migratory divide and hybridised extensively. Within migratory divides, overwintering habitats were associated with assortative mating, implicating a central contribution of divergent migratory behaviour to reproductive barriers. The remarkable geographic coincidence between migratory divides and genetic breaks supports a long-standing hypothesis that the Tibetan Plateau is a substantial barrier contributing to the diversity of Siberian avifauna.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.13420","usgsCitation":"Scordato, E., Smith, C.A., Semenov, G.A., Yu, L., Wilkins, M.R., Liang, W., Rubtsov, A., Sundev, G., Koyama, K., Turbek, S.P., Wunder, M., Stricker, C.A., and Safran, R., 2020, Migratory divides coincide with reproductive barriers across replicated avian hybrid zones above the Tibetan Plateau: Ecology Letters, v. 23, no. 2, p. 231-241, https://doi.org/10.1111/ele.13420.","productDescription":"11 p.","startPage":"231","endPage":"241","ipdsId":"IP-101774","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":458500,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.13420","text":"Publisher Index Page"},{"id":437203,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C2TH2K","text":"USGS data release","linkHelpText":"Stable carbon and nitrogen isotope data for Siberian barn 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Colorado","active":true,"usgs":false}],"preferred":false,"id":838887,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wunder, Michael B.","contributorId":80599,"corporation":false,"usgs":false,"family":"Wunder","given":"Michael B.","affiliations":[{"id":6674,"text":"Department of Integrative Biology, University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":838888,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838889,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Safran, Rebecca","contributorId":289295,"corporation":false,"usgs":false,"family":"Safran","given":"Rebecca","email":"","affiliations":[{"id":62097,"text":"The University of Colorado","active":true,"usgs":false}],"preferred":false,"id":838890,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70236239,"text":"70236239 - 2020 - Directivity of M 3.1 earthquake near Anza, California and the effect on peak ground motion","interactions":[],"lastModifiedDate":"2022-08-31T14:19:50.242428","indexId":"70236239","displayToPublicDate":"2019-11-19T09:14:00","publicationYear":"2020","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":"Directivity of M 3.1 earthquake near Anza, California and the effect on peak ground motion","docAbstract":"We show the effect of rupture directivity on peak ground‐motion values for a moderate magnitude event at Anza, California, and neighboring stations at the Imperial Valley. The event was located near Borrego Springs on the west side of the Salton Sea and was well recorded at broadband stations near Anza, California, and at stations on the west side of the Imperial Valley. After correcting for regional attenuation, an anomalously large residual in peak motion was observed at station ERR just to the southeast of the epicenter. Using the algorithm from Boatwright (2007), peak motions from the regional seismic networks in southern California were inverted to determine directivity, which was to the southeast along the trend of the San Jacinto fault toward station ERR. This algorithm uses peak values compiled for the ShakeMap system mostly at regional distances. It does not capture the main features of the source time function (STF) predicted by directivity. Consequently, we determined the second‐degree moments for this earthquake, which confirmed that station ERR has a shorter and higher STF compared to stations to the northwest suggesting rupture propagated to the southeast. The azimuthal distribution of local stations is sparse, but nevertheless the largest amplitudes (such as at station ERR) correlate well with the maximum in the radiation pattern and smaller values with the minima, which is the radiation pattern for SH plus the effect of directivity. Using the data from the analysis of the second‐degree moments, the characteristic length of the fault is 0.58 km, assuming an idealized unilateral extended rupture with a rupture time of 0.09 s. This yields an apparent rupture velocity of
Prominent scarps on Pinedale glacial surfaces along the eastern base of the Teton Range confirm latest Pleistocene to Holocene surface‐faulting earthquakes on the Teton fault, but the timing of these events is only broadly constrained by a single previous paleoseismic study. We excavated two trenches at the Leigh Lake site near the center of the Teton fault to address open questions about earthquake timing and rupture length. Structural and stratigraphic evidence indicates two surface‐faulting earthquakes at the site that postdate deglacial sediments dated by radiocarbon and optically stimulated luminescence to ∼10–11 ka. Earthquake LL2 occurred at ∼10.0 ka (9.7–10.4 ka; 95% confidence range) and LL1 at ∼5.9 ka (4.8–7.1 ka; 95%). LL2 predates an earthquake at ∼8ka identified in the previous paleoseismic investigation at Granite Canyon. LL1 corresponds to the most recent Granite Canyon earthquake at ∼4.7–7.9 ka (95% confidence range). Our results are consistent with the previously documented long‐elapsed time since the most recent Teton fault rupture and expand the fault’s earthquake history into the early Holocene.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120190129","usgsCitation":"Zellman, M., DuRoss, C., Thackray, G.R., Personius, S., Reitman, N.G., Mahan, S.A., and Brossy, C., 2020, Holocene rupture history of the central Teton fault at Leigh Lake; Grand Teton National Park, Wyoming: Bulletin of the Seismological Society of America, v. 110, no. 1, p. 67-82, https://doi.org/10.1785/0120190129.","productDescription":"16 p.","startPage":"67","endPage":"82","ipdsId":"IP-111443","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":370540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.928955078125,\n 43.671844983221604\n ],\n [\n -110.38238525390625,\n 43.671844983221604\n ],\n [\n -110.38238525390625,\n 44.12702800650004\n ],\n [\n -110.928955078125,\n 44.12702800650004\n ],\n [\n -110.928955078125,\n 43.671844983221604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Zellman, Mark","contributorId":167020,"corporation":false,"usgs":false,"family":"Zellman","given":"Mark","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":778161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DuRoss, Christopher B. 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thackray, Glenn R.","contributorId":221430,"corporation":false,"usgs":false,"family":"Thackray","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":40375,"text":"Department of Geosciences, Idaho State University","active":true,"usgs":false}],"preferred":false,"id":778163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Personius, Stephen 0000-0001-8347-7370 personius@usgs.gov","orcid":"https://orcid.org/0000-0001-8347-7370","contributorId":150055,"corporation":false,"usgs":true,"family":"Personius","given":"Stephen","email":"personius@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":778166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brossy, Cooper","contributorId":221431,"corporation":false,"usgs":false,"family":"Brossy","given":"Cooper","affiliations":[],"preferred":false,"id":778167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217797,"text":"70217797 - 2020 - Estimating population size with imperfect detection using a parametric bootstrap","interactions":[],"lastModifiedDate":"2021-02-03T12:40:10.682479","indexId":"70217797","displayToPublicDate":"2019-11-19T06:38:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1577,"text":"Environmetrics","active":true,"publicationSubtype":{"id":10}},"title":"Estimating population size with imperfect detection using a parametric bootstrap","docAbstract":"We develop a novel method of estimating population size from imperfectly detected counts of individuals and a separate estimate of detection probability. Observed counts are separated into classes within which detection probability is assumed constant. Within a detection class, counts are modeled as a single binomial observation X with success probability p where the goal is to estimate index N. We use a Horvitz–Thompson‐like estimator for N and account for uncertainty in both sample data and estimated success probability via a parametric bootstrap. Unlike capture–recapture methods, our model does not require repeated sampling of the population. Our method is able to achieve good results, even with small X. We show in a factorial simulation study that the median of the bootstrapped sample has small bias relative to N and that coverage probabilities of confidence intervals for N are near nominal under a wide array of scenarios. Our methodology begins to break down when P(X=0)>0.1 but is still capable of obtaining reasonable confidence coverage. We illustrate the proposed technique by estimating (1) the size of a moose population in Alaska and (2) the number of bat fatalities at a wind power facility, both from samples with imperfect detection probabilities, estimated independently.
Pinyon-juniper (PJ) plant communities cover a large area across North America and provide critical habitat for wildlife, biodiversity and ecosystem functions, and rich cultural resources. These communities occur across a variety of environmental gradients, disturbance regimes, structural conditions and species compositions, including three species of juniper and two species of pinyon. PJ communities have experienced substantial changes in recent decades and identifying appropriate management strategies for these diverse communities is a growing challenge. Here, we surveyed the literature and compiled 441 studies to characterize patterns in research on PJ communities through time, across geographic space and climatic conditions, and among focal species. We evaluate the state of knowledge for three focal topics: 1) historical stand dynamics and responses to disturbance, 2) land management actions and their effects, and 3) potential future responses to changing climate. We identified large and potentially important gaps in our understanding of pinyon-juniper communities both geographically and topically. The effect of drought on Pinus edulis, the pinyon pine species in eastern PJ communities was frequently addressed, while few studies focused on drought effects on Pinus monophylla, which occurs in western PJ communities. The largest proportion of studies that examined land management actions only measured their effects for one year. Grazing was a common land-use across the geographic range of PJ communities yet was rarely studied. We found only 39 studies that had information on the impacts of anthropogenic climate change and most were concentrated on Pinus edulis. These results provide a synthetic perspective on PJ communities that can help natural resource managers identify relevant knowledge needed for decision-making and researchers design new studies to fill important knowledge gaps.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2019.117628","usgsCitation":"Hartsell, J.A., Copeland, S., Munson, S.M., Butterfield, B.J., and Bradford, J., 2020, Gaps and hotspots in the state of knowledge of pinyon-juniper communities: Forest Ecology and Management, v. 455, 117628, 23 p., https://doi.org/10.1016/j.foreco.2019.117628.","productDescription":"117628, 23 p.","ipdsId":"IP-108384","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458505,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2019.117628","text":"Publisher Index Page"},{"id":437204,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LWZN72","text":"USGS data release","linkHelpText":"Pinyon and Juniper location data, including a literature review citation list of Pinyon-Juniper systems from 1909 to 2018"},{"id":377388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.47998046875,\n 32.491230287947594\n ],\n [\n -103.90869140625,\n 36.79169061907076\n ],\n [\n -104.5458984375,\n 40.329795743702064\n ],\n [\n -110.8740234375,\n 40.697299008636755\n ],\n [\n -111.86279296875,\n 41.60722821271717\n ],\n [\n -116.05957031249999,\n 41.45919537950706\n ],\n [\n -119.81689453125,\n 37.59682400108367\n ],\n [\n -117.35595703124999,\n 34.939985151560435\n ],\n [\n -112.4560546875,\n 32.43561304116276\n ],\n [\n -109.2041015625,\n 31.466153715024294\n ],\n [\n -108.2373046875,\n 31.372399104880525\n ],\n [\n -108.17138671875,\n 31.784216884487385\n ],\n [\n -104.19433593749999,\n 31.952162238024975\n ],\n [\n -104.47998046875,\n 32.491230287947594\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"455","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hartsell, Jessica A. 0000-0003-1414-8797","orcid":"https://orcid.org/0000-0003-1414-8797","contributorId":238016,"corporation":false,"usgs":true,"family":"Hartsell","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":795820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":795821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":795822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795823,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}