The Biodiversity and Climate Change Virtual Laboratory: Where ecology meets big data

被引:55
作者
Hallgren, Willow [1 ]
Beaumont, Linda [2 ]
Bowness, Andrew [1 ]
Chambers, Lynda [3 ]
Graham, Erin [4 ,5 ]
Holewa, Hamish [1 ]
Laffan, Shawn [6 ]
Mackey, Brendan [1 ]
Nix, Henry [7 ]
Price, Jeff [8 ]
Vanderwal, Jeremy [4 ,5 ]
Warren, Rachel [8 ]
Weis, Gerhard [1 ]
机构
[1] Griffith Univ, Gold Coast Campus,Parklands Dr, Southport, Qld 4215, Australia
[2] Macquarie Univ, Fac Sci & Engn, Dept Biol Sci, N Ryde, NSW 2109, Australia
[3] Bur Meteorol, Melbourne, Vic 3001, Australia
[4] James Cook Univ, eRes, Townsville, Qld 4810, Australia
[5] James Cook Univ, Ctr Trop Biodivers & Climate Change, Townsville, Qld 4810, Australia
[6] Univ New S Wales, Sch Biol Earth & Environm Sci, Ctr Ecosyst Sci, Sydney, NSW 2052, Australia
[7] Australian Natl Univ, Fenner Sch Environm & Soc, Bldg 141 Linnaeus Way, Canberra, ACT 2601, Australia
[8] Univ E Anglia, Sch Environm Sci, Tyndall Ctr Climate Change Res, Norwich NR4 7TJ, Norfolk, England
关键词
Biodiversity; Climate change; Virtual Laboratory; Species distribution modelling;
D O I
10.1016/j.envsoft.2015.10.025
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets; and a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration, cross-fertilisation of ideas, and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution, modelling, and reducing time spent on such research, are being met. (C) 2015 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:182 / 186
页数:5
相关论文
共 15 条
[11]   Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology [J].
Peters, Debra P. C. ;
Havstad, Kris M. ;
Cushing, Judy ;
Tweedie, Craig ;
Fuentes, Olac ;
Villanueva-Rosales, Natalia .
ECOSPHERE, 2014, 5 (06)
[12]   Maximum entropy modeling of species geographic distributions [J].
Phillips, SJ ;
Anderson, RP ;
Schapire, RE .
ECOLOGICAL MODELLING, 2006, 190 (3-4) :231-259
[13]   Web technologies for environmental Big Data [J].
Vitolo, Claudia ;
Elkhatib, Yehia ;
Reusser, Dominik ;
Macleod, Christopher J. A. ;
Buytaert, Wouter .
ENVIRONMENTAL MODELLING & SOFTWARE, 2015, 63 :185-198
[14]   Development and illustrative outputs of the Community Integrated Assessment System (CIAS), a multi-institutional modular integrated assessment approach for modelling climate change [J].
Warren, R. ;
Santos, S. de la Nava ;
Arnell, N. W. ;
Bane, M. ;
Barker, T. ;
Barton, C. ;
Ford, R. ;
Fuessel, H.-M. ;
Hankin, Robin K. S. ;
Klein, Rupert ;
Linstead, C. ;
Kohler, J. ;
Mitchell, T. D. ;
Osborn, T. J. ;
Pan, H. ;
Raper, S. C. B. ;
Riley, G. ;
Schellnhueber, H. J. ;
Winne, S. ;
Anderson, D. .
ENVIRONMENTAL MODELLING & SOFTWARE, 2008, 23 (05) :592-610
[15]   Interpretation of high projections for global-mean warming [J].
Wigley, TML ;
Raper, SCB .
SCIENCE, 2001, 293 (5529) :451-454