308 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. DOI: 10.4018/978-1-61692-871-1.ch015 Chapter 15 Computational Techniques for Biologic Species Distribution Modeling Pedro Luiz Pizzigatti Corrêa Agricultural Automation Laboratory, Polytechnic School of the University of Sao Paulo, EPUSP, Brazil Mariana Aparecida Carvalhaes Brazilian Agricultural Research Corporation, EMBRAPA Middle-North, Brazil Antonio Mauro Saraiva Agricultural Automation Laboratory, Polytechnic School of the University of Sao Paulo, EPUSP, Brazil Fabrício Augusto Rodrigues Agricultural Automation Laboratory, Polytechnic School of the University of Sao Paulo, EPUSP, Brazil Elisângela Silva da Cunha Rodrigues Agricultural Automation Laboratory, Polytechnic School of the University of Sao Paulo, EPUSP, Brazil Ricardo Luis de Azevedo da Rocha Laboratory of Languages and Adaptive Techniques, Polytechnic School of the University of Sao Paulo, EPUSP, Brazil AbSTRAcT Computational modeling techniques for species geographic distribution are critical to support the task of identifying areas with high risk of loss of Biodiversity. These tools can assist in the conservation of Biodiversity, in planning the use of non-inhabited regions, in estimating the risk of invasive species, in the proposed reintroduction programs for species and even in planning the protecting endangered species. Furthermore, such techniques can help to understand the effects of climate change and other changes in the geographical distribution of species. This chapter presents concepts related to the species distribution modeling and algorithms based on Neural Networks and Maximum Entropy as alternatives