The Benefits of a Compute Cluster Approach to High Spatial Resolution Biodiversity Richness Modelling: Projecting the Impact of Climate Change on Mediterranean Flora
High spatial resolution environmental data gives us a better understanding of the environmental factors affecting plant distributions at fine spatial scales. However, large environmental datasets dramatically increase compute times and output species model size stimulating the need for an alternative computing solution. Cluster computing offers such a solution, by allowing both multiple plant species Environmental Niche Models (ENMs) and individual tiles of high spatial resolution models to be computed concurrently on the same compute cluster. We apply our methodology to a case study of 4,209 species of Mediterranean flora (around 17% of species believed present in the biome). We demonstrate a 16 times speed-up of ENM computation time when 16 CPUs were used on the compute cluster. Our custom Java ‘Merge’ and ‘Downsize’ programs reduce ENM output files sizes by 94%. The median 0.98 test AUC score of species ENMs is aided by various species occurrence data filtering techniques. Finally, by calculating the percentage change of individual grid cell values, we map the projected percentages of plant species vulnerable to climate change in the Mediterranean region between 1950–2000 and 2020.
||Theme: Scientific Evidence, Climate Change, Mediterranean Flora, Environmental Niche Models, Biodiversity Richness, High Spatial Resolution, Maxent, Compute Cluster, Parallel Computing
The International Journal of Climate Change: Impacts and Responses, Volume 4, Issue 1, pp.115-128.
Article: Print (Spiral Bound).
Article: Electronic (PDF File; 10.919MB).
Ph.D. Student, Centre for Plant Diversity and Systematics, Plant Science Laboratories, School of Biological Sciences, University of Reading, Reading, Berkshire, UK
A Ph.D. student in the School of Biological Sciences at
the University of Reading, Marshall J. Heap is working on a biodiversity climatic change study of Plants in the Mediterranean region. Heap & Culham (2010) entitled ‘Automated Pre-processing Strategies for Species Occurrence Data Used in Biodiversity Modelling’, is the first published work regarding this project.
Marshall has the following academic degrees:
B.Sc. Computing (Hons) First Class, The Open University, U.K.
M.Sc. (Distinction) Geographical Information Science, Birkbeck College,University of London, U.K.
Lecturer, Centre for Plant Diversity and Systematics, Plant Science Laboratories, School of Biological Sciences, University of Reading, Reading, Berkshire, UK
Alastair Culham is a plant systematist and Director of the M.Sc. in Plant Diversity at University of Reading. His research combines molecular phylogenetic studies with niche modelling and with novel species identification
aids to help define species and understand their interaction with the environment. Under BBSRC funding he instigated and co-developed techniques
of phyloclimatic modelling that brought together the evolution of species
in relation to their changing climate measured over and modelled over millions of years. Under NERC funding he developed the first case study using
molecular phylogenetics to evaluate critically the competing hypotheses of
vicariance versus long-distance dispersal to explain distribution in a genus of
flowering plants. His work in plant taxonomy and niche modelling includes
some 200 publications.
Alastair has the following academic degrees:
B.Sc. Botany (Hons) First Class, University of Reading, U.K.
Ph.D. Taxonomy of the Droseraceae, University of Leicester, U.K.
Training and Outreach Mentor, High Performance Computing, Glamorgan, Wales, UK
James took up his post in January 2012 and is based at the University of Glamorgan. Before joining HPC Wales, James was Condor Project Manager and Application Support Engineer in the Advanced Research Computing centre at Cardiff University (ARCCA) and, prior to that, Condor Project Manager for Cardiff University’s Information Services.
During his time in ARCCA, James was responsible for growing and creating the largest academic condor pool in the U.K. with over 8,000 processor cores, as well as helping users with a range of requests on both the Condor and Merlin systems, Merlin being a 2048 core High Performance Computer. For the last 2 years of his time in ARCCA, he was also responsible for developing and organising the ARCCA training programme.
James obtained his B.Sc. in Computer Science in 2001, and his Ph.D. in Computer Science with a focus on Visualisation and Grid Computing in 2005, both from the University of Hull.
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