ALBERT

Description: Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space ( i.e . into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Description: ABSTRACT Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change impact study. The selection of climate models is not straightforward and can be done by following different methods. Usually, the selection is either based on the entire range of changes in climatic variables as projected by the total ensemble of available climate models or on the skill of climate models to simulate past climate. The present study combines these approaches in a three-step sequential climate model selection procedure: (1) initial selection of climate models based on the range of projected changes in climatic means, (2) refined selection based on the range of projected changes in climatic extremes and (3) final selection based on the climate model skill to simulate past climate. This procedure is illustrated for a study area covering the Indus, Ganges and Brahmaputra river basins. Subsequently, the changes in climate between 1971–2000 and 2071–2100 are analysed, showing that the future climate projections in this area are highly uncertain but that changes are imminent.

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Schoggins, John W -- MacDuff, Donna A -- Imanaka, Naoko -- Gainey, Maria D -- Shrestha, Bimmi -- Eitson, Jennifer L -- Mar, Katrina B -- Richardson, R Blake -- Ratushny, Alexander V -- Litvak, Vladimir -- Dabelic, Rea -- Manicassamy, Balaji -- Aitchison, John D -- Aderem, Alan -- Elliott, Richard M -- Garcia-Sastre, Adolfo -- Racaniello, Vincent -- Snijder, Eric J -- Yokoyama, Wayne M -- Diamond, Michael S -- Virgin, Herbert W -- Rice, Charles M -- K01 DK095031/DK/NIDDK NIH HHS/ -- R00 AI095320/AI/NIAID NIH HHS/ -- R01 AI032972/AI/NIAID NIH HHS/ -- R01 AI091707/AI/NIAID NIH HHS/ -- R01 AI102597/AI/NIAID NIH HHS/ -- R01 AI104972/AI/NIAID NIH HHS/ -- England -- Nature. 2015 Sep 3;525(7567):144. doi: 10.1038/nature14555. Epub 2015 Jul 8.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26153856" target="_blank"〉PubMed〈/a〉

Description: Understanding ecological patterns and response to climate change requires unbiased data on species distribution. This can be challenging, especially in biodiverse but extreme environments like the Himalaya. This study presents the results of the first ever application of Unmanned Aerial Systems (UAS) imagery for species-level mapping of vegetation in the Himalaya following a hierarchical Geographic Object Based Image Analysis (GEOBIA) method. The first level of classification separated green vegetated objects from the rest with overall accuracy of 95%. At the second level, seven cover types were identified (including four woody vegetation species). For this, the suitability of various spectral, shape and textural features were tested for classifying them using an ensemble decision tree algorithm. Spectral features alone yielded ~70% accuracy (kappa 0.66) whereas adding textural and shape features marginally improved the accuracy (73%) but at the cost of a substantial increase in processing time. Contrast in plant morphological traits was the key to distinguishing nearby stands as different species. Hence, broad-leaved versus fine needle leaved vegetation were mapped more accurately than structurally similar classes such as Rhododendron anthopogon versus non-photosynthetic vegetation. Results highlight the potential and limitations of the suggested UAS-GEOBIA approach for detailed mapping of plant communities and suggests future research directions.

Description: ABSTRACT The Koshi river basin is a sub-basin of the Ganges shared among China, Nepal, and India. The river system has a high potential for investment in hydropower development and for irrigation in downstream areas. The upper part of the basin contains a substantial reserve of freshwater in the form of snow and glaciers. Climate variability, climate change, and climate extremes might impact on these reserves, and in turn impact on systems that support livelihoods, such as agriculture, biodiversity and related ecosystem services. Climatological variability and trends over the Koshi river basin were studied using RClimDex. Daily temperature data (20 stations) and precipitation data (50 stations) from 1975 to 2010 were used in the analysis. The results show that the frequency and intensity of weather extremes are increasing. The daily maximum temperature (TXx) increased by 0.1 °C decade −1 on average between 1975 and 2010 and the minimum (TNn) by 0.3 °C decade −1 . The number of warm nights increased at all stations. Most of the extreme temperature indices showed a consistently different pattern in the mountains than in the Indo-Gangetic plains, although not all results were statistically significant. The warm days (TX90p), warm nights (TN90p), warm spell duration (WSDI), and diurnal temperature range (DTR) increased at most of the mountain stations; whereas monthly maximum and minimum values of daily maximum temperature, TX90p, cool nights (TN10p), WSDI, cold spell duration indicator (CSDI), DTR decreased at the stations in the Indo-Gangetic plains, while the number of cold days increased. There was an increase in total annual rainfall and rainfall intensity, although no clear long-term linear trend, whereas the number of consecutive dry days increased at almost all stations. The results indicate that the risk of extreme climate events over the basin is increasing, which will increase people's vulnerability and has strong policy implications.

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Shrestha, Uttam Babu -- Shrestha, Sujata -- Aryal, Achyut -- New York, N.Y. -- Science. 2015 Aug 14;349(6249):699-700. doi: 10.1126/science.349.6249.699-b.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Institute for Agriculture and the Environment (IAgE), University of Southern Queensland, Toowoomba, QLD 4350, Australia. ubshrestha@yahoo.com. ; Department of Biology, University of Massachusetts Boston, Boston, MA 02125, USA. ; Institute of Natural and Mathematical Sciences, Massey University, Albany 0745, Auckland, New Zealand.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26273047" target="_blank"〉PubMed〈/a〉

Description: The Gorkha earthquake (magnitude 7.8) on 25 April 2015 and later aftershocks struck South Asia, killing ~9000 people and damaging a large region. Supported by a large campaign of responsive satellite data acquisitions over the earthquake disaster zone, our team undertook a satellite image survey of the earthquakes' induced geohazards in Nepal and China and an assessment of the geomorphic, tectonic, and lithologic controls on quake-induced landslides. Timely analysis and communication aided response and recovery and informed decision-makers. We mapped 4312 coseismic and postseismic landslides. We also surveyed 491 glacier lakes for earthquake damage but found only nine landslide-impacted lakes and no visible satellite evidence of outbursts. Landslide densities correlate with slope, peak ground acceleration, surface downdrop, and specific metamorphic lithologies and large plutonic intrusions.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Kargel, J S -- Leonard, G J -- Shugar, D H -- Haritashya, U K -- Bevington, A -- Fielding, E J -- Fujita, K -- Geertsema, M -- Miles, E S -- Steiner, J -- Anderson, E -- Bajracharya, S -- Bawden, G W -- Breashears, D F -- Byers, A -- Collins, B -- Dhital, M R -- Donnellan, A -- Evans, T L -- Geai, M L -- Glasscoe, M T -- Green, D -- Gurung, D R -- Heijenk, R -- Hilborn, A -- Hudnut, K -- Huyck, C -- Immerzeel, W W -- Liming, Jiang -- Jibson, R -- Kaab, A -- Khanal, N R -- Kirschbaum, D -- Kraaijenbrink, P D A -- Lamsal, D -- Shiyin, Liu -- Mingyang, Lv -- McKinney, D -- Nahirnick, N K -- Zhuotong, Nan -- Ojha, S -- Olsenholler, J -- Painter, T H -- Pleasants, M -- Pratima, K C -- Yuan, Q I -- Raup, B H -- Regmi, D -- Rounce, D R -- Sakai, A -- Donghui, Shangguan -- Shea, J M -- Shrestha, A B -- Shukla, A -- Stumm, D -- van der Kooij, M -- Voss, K -- Xin, Wang -- Weihs, B -- Wolfe, D -- Lizong, Wu -- Xiaojun, Yao -- Yoder, M R -- Young, N -- New York, N.Y. -- Science. 2016 Jan 8;351(6269):aac8353. doi: 10.1126/science.aac8353. Epub 2015 Dec 16.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, USA. kargel@hwr.arizona.edu dshugar@uw.edu uharitashya1@udayton.edu. ; Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, USA. ; School of Interdisciplinary Arts and Sciences, University of Washington Tacoma, Tacoma, WA, USA. kargel@hwr.arizona.edu dshugar@uw.edu uharitashya1@udayton.edu. ; Department of Geology, University of Dayton, Dayton, OH, USA. kargel@hwr.arizona.edu dshugar@uw.edu uharitashya1@udayton.edu. ; Ministry of Forests, Lands and Natural Resource Operations, Prince George, BC, Canada. ; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. ; Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan. ; Scott Polar Research Institute, University of Cambridge, Cambridge, UK. ; Institute of Environmental Engineering, Federal Institute of Technology-ETH, Zurich, Switzerland. ; NASA Marshall Space Flight Center, Huntsville, AL, USA. ; International Centre for Integrated Mountain Development, Kathmandu, Nepal. ; NASA Headquarters, Washington, DC, USA. ; GlacierWorks, Marblehead, MA, USA. ; The Mountain Institute, Elkins, WV, USA. ; U.S. Geological Survey, Menlo Park, CA, USA. ; Central Department of Geology, Tribhuvan University, Kirtipur, Kathmandu, Nepal. ; Department of Geography, University of Victoria, Victoria, BC, Canada. ; CVA Engineering, Suresnes, France. ; Earthquake Science Center, U.S. Geological Survey, Pasadena, CA, USA. ; ImageCat, Long Beach, CA, USA. ; Faculty of Geosciences, Utrecht University, Utrecht, Netherlands. ; State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, Hubei Province, China. ; U.S. Geological Survey, Golden, CO, USA. ; Department of Geosciences, University of Oslo, Blindern, Oslo, Norway. ; Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA. ; Cold and Arid Regions of Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China. ; School of Earth Sciences and Engineering, Nanjing University, Nanjing, China. ; Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX, USA. ; School of Geography Science, Nanjing Normal University, Nanjing, China. ; Department of Geography, Texas A&M University, College Station, TX, USA. ; Department of Geology, University of Dayton, Dayton, OH, USA. ; Arizona Remote Sensing Center, School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA. ; National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA. ; Himalayan Research Center, Kathmandu, Nepal. ; Environmental and Water Resources Engineering, University of Texas at Austin, Austin, TX, USA. ; Wadia Institute of Himalayan Geology, Dehradun, India. ; MacDonald Dettwiler and Associates-GSI, Ottawa, Ontario, Canada. ; Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, USA. ; College of Architecture and Urban Planning, Hunan University of Science and Technology, Xiangtan, China. ; Geography Department, Kansas State University, Manhattan, KS, USA. ; Global Land Ice Measurements from Space (GLIMS) Steward, Alaska Region, Anchorage, AK, USA. ; College of Geographical Science and Environment, Northwest Normal University, China. ; Department of Physics, University of California, Davis, Davis, CA, USA. ; Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, TAS, Australia.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26676355" target="_blank"〉PubMed〈/a〉