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

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. BCCVL facilitates and expedites modelling of climate change's impact on biodiversity.BCCVL integrates numerous species distribution modelling tools and myriad datasets.BCCVL negates the need for advanced programming and modelling expertise.BCCVL allows for increases in productivity and complexity of experimental design.BCCVL facilitates the sharing of data promoting transparency in the research process.

[1]  S. B. McDowell,et al.  Atlas of elapid snakes of Australia , 1987 .

[2]  T. Wigley,et al.  Interpretation of High Projections for Global-Mean Warming , 2001, Science.

[3]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[4]  Jeroen Van Den Muyzenberg,et al.  Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic , 1999 .

[5]  Alexei G. Sankovski,et al.  Special report on emissions scenarios : a special report of Working group III of the Intergovernmental Panel on Climate Change , 2000 .

[6]  Olac Fuentes,et al.  Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology , 2014 .

[7]  Graham D. Riley,et al.  Development and illustrative outputs of the Community Integrated Assessment System (CIAS), a multi-institutional modular integrated assessment approach for modelling climate change , 2008, Environ. Model. Softw..

[8]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[9]  L. K. Gohar,et al.  How difficult is it to recover from dangerous levels of global warming? , 2009 .

[10]  Yehia El-khatib,et al.  Web technologies for environmental Big Data , 2015, Environ. Model. Softw..

[11]  Shawn W. Laffan,et al.  Biodiverse, a tool for the spatial analysis of biological and related diversity , 2010 .

[12]  T. D. Mitchell,et al.  Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates , 2003 .

[13]  C. Lu,et al.  using artificial neural network , 2014 .