Species distribution modeling in the cloud. (11th July 2013)

• Record Type: Journal Article
• Authors: Candela, Leonardo; Castelli, Donatella; Coro, Gianpaolo; Pagano, Pasquale; Sinibaldi, Fabio
• Other Names: Simmhan Yogesh guestEditor.; Ramakrishnan Lavanya guestEditor.; Antoniu Gabriel guestEditor.; Goble Carole guestEditor.; Yu Yong guestEditor.; Mu Yi guestEditor.; Lu Rongxing guestEditor.; Ren Jian guestEditor.; Venticinque Salvatore guestEditor.; Camacho David guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 4(2016)
• Page Start: 1056
• Page End: 1079
• Publication Date: 2013-07-11
• Subjects: species distribution modeling -- Hybrid Data Infrastructure -- cloud computing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3030
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Species distribution modeling is a process aiming at computationally predicting the distribution of species in geographic areas on the basis of environmental parameters including climate data. Such a quantitative approach has a lot of potentialities in many areas that include setting up conservation priorities, testing biogeographic hypotheses, and assessing the impact of accelerated land use. To further promote the diffusion of such an approach, it is fundamental to develop a flexible, comprehensive, and robust environment capable of enabling practitioners and communities of practice to produce species distribution models more efficiently. A promising way to build such an environment is offered by modern infrastructures promoting the sharing of resources, including hardware, software, data, and services. This paper describes an approach to species distribution modeling based on a Hybrid Data Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud). It discusses the whole set of services needed to support the phases of such a complex process including access to occurrence records and environmental parameters and the processing of such information to predict the probability of a species' occurrence in given areas.Copyright © 2013 John Wiley & Sons, Ltd.