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openModeller - an open framework for ecological niche modeling: analysis and future improvements

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006  701P0606.(doi:10.13031/2013.21974)
Authors:   F. S. Santana, R. R. Fonseca, A. M. Saraiva, P. L. P. Correa, C. Bravo, R. De Giovanni
Keywords:   Information systems and database, biodiversity informatics, openModeller, ecological niche modeling

Ecological niche models play an import role in species distribution prediction. They provide ways to study biodiversity distribution, past and present, to understand its causes, and to propose scenarios and strategies for sustainable use and for preservation initiatives. Ecological niche modeling usually involves many algorithms, environmental variables, data types and formats, and both pre and post analysis work. That complexity forces modeling experts to know and to use many software packages and to manually solve technical issues. openModeller was conceived as a framework to provide an open, standardized and integrated infrastructure for accommodating geo-referencing issues, environmental data and algorithms deployment, allowing researchers to focus on the modeling problem and on other high-level analysis. This paper presents an analysis of openModeller describing its main use cases and interactions. It also suggests improvements in the current software to reduce its learning curve and to increase system usability through a set of modifications. The set of changes adds the following features: (1) new storage component for species, geographic and environmental data, (2) new data import mechanisms, (3) graphical user interface improvements, and (4) new documentation about installing and using the software. These recommendations result from a first analysis of openModeller as part of a major project concerned with species occurrence data, environmental data, dataset preparation, modeling algorithms, pre and postanalysis, component-based software development, high performance computing, web services, and multiple interfaces in order to make the framework accessible to a larger range of users.

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