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Model-R: a framework for scalable and reproducible ecological niche modeling
Sánchez-Tapia, A.; de Siqueira, M.F.; Lima, R.O.; Barros, F.S.M.; Gall, G.M.; Gadelha, L.M.R.; da Silva, L.A.E.; Osthoff, C. (2018). Model-R: a framework for scalable and reproducible ecological niche modeling, in: Mocskos, E. et al. High performance computing. 4th Latin American Conference, CARLA 2017, Buenos Aires, Argentina, and Colonia del Sacramento, Uruguay, September 20-22, 2017, Revised Selected Papers. Communications in Computer and Information Science, 796: pp. 218-232. https://dx.doi.org/10.1007/978-3-319-73353-1_15
In: Mocskos, E.; Nesmachnow, S. (Ed.) (2018). High performance computing. 4th Latin American Conference, CARLA 2017, Buenos Aires, Argentina, and Colonia del Sacramento, Uruguay, September 20-22, 2017, Revised Selected Papers. Communications in Computer and Information Science, 796. Springer International Publishing: Cham. ISBN 978-3-319-73352-4; e-ISBN 978-3-319-73353-1. xiv, 432 pp. https://dx.doi.org/10.1007/978-3-319-73353-1
In: Communications in Computer and Information Science. Springer: Cham. ISSN 1865-0929; e-ISSN 1865-0937
Beschikbaar in  Auteurs 
Documenttype: Congresbijdrage
Author keywords
    Species distribution modeling; Ecological niche modeling; Science gateways; Scalability; Provenance
Auteurs  Top 
  • Sánchez-Tapia, A.
  • de Siqueira, M.F.
  • Lima, R.O.
  • Barros, F.S.M.
  • Gall, G.M.
  • Gadelha, L.M.R.
  • da Silva, L.A.E.
  • Osthoff, C.
Abstract
    Spatial analysis tools and synthesis of results are key to identifying the best solutions in biodiversity conservation. The importance of process automation is associated with increased efficiency and performance both in the data pre-processing phase and in the post-analysis of the results generated by the packages and modeling programs. The Model-R framework was developed with the main objective of unifying pre-existing ecological niche modeling tools into a common framework and building a web interface that automates steps of the modeling process and occurrence data retrieval. The web interface includes RJabot, a functionality that allows for searching and retrieving occurrence data from Jabot, the main reference on botanical collections management system in Brazil. It returns data in a suitable format to be consumed by other components of the framework. Currently, the tools are multi-projection, they can thus be applied to different sets of temporal and spatial data. Model-R is also multi-algorithm, with seven algorithms available for modeling: BIOCLIM, Mahalanobis distance, Maxent, GLM, RandomForest, SVM, and DOMAIN. The algorithms as well as the entire modeling process may be parametrized using command-line tools or through the web interface. We hope that the use of this application, not only by modeling specialists but also as a tool for policy makers, will be a significant contribution to the continuous development of biodiversity conservation analysis. The Model-R web interface can be installed locally or on a server. A software container is provided to automate the installation.
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