Computacional techniques for biologic species distribution modeling

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

[1]  David R. B. Stockwell,et al.  The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..

[2]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[3]  T. Yee,et al.  Generalized additive models in plant ecology , 1991 .

[4]  Peter Brewer,et al.  openModeller: a generic approach to species’ potential distribution modelling , 2011, GeoInformatica.

[5]  Michael Drielsma,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling , 2002, Biodiversity & Conservation.

[6]  David G. Green,et al.  Parallel computing in ecological simulation , 1990 .

[7]  B. Efron.  Bootstrap Methods: Another Look at the Jackknife , 1979 .

[8]  J. Busby.  A biogeoclimatic analysis of Nothofagus cunninghamii (Hook.) Oerst. in southeastern Australia , 1986 .

[9]  Miroslav Dudík,et al.  A maximum entropy approach to species distribution modeling , 2004, ICML.

[10]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[11]  Fabiana Soares Santana,et al.  A reference business process for ecological niche modelling , 2008, Ecol. Informatics.

[12]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[13]  David Stockwell.  Machine learning and the problem of prediction and explanation in ecological modelling , 1992 .

[14]  A. O. Nicholls,et al.  Determining species response functions to an environmental gradient by means of a β‐function , 1994 .

[15]  D. Mitja,et al.  Establishment of babassu in pastures in Pará, Brazil. , 2001 .

[16]  Adwait Ratnaparkhi,et al.  A Simple Introduction to Maximum Entropy Models for Natural Language Processing , 1997 .

[17]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[18]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[19]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[20]  M. H. Quenouille.  NOTES ON BIAS IN ESTIMATION , 1956 .

[21]  E. Jaynes.  On the rationale of maximum-entropy methods , 1982, Proceedings of the IEEE.

[22]  R. Piñeiro,et al.  Ecology matters: Atlantic–Mediterranean disjunction in the sand‐dune shrub Armeria pungens (Plumbaginaceae) , 2007, Molecular ecology.

[23]  David R. B. Stockwell,et al.  Induction of sets of rules from animal distribution data: a robust and informative method of data analysis , 1992 .

[24]  G. Carpenter,et al.  DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals , 1993, Biodiversity & Conservation.

[25]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.