%0 Journal Article %1 Stockwell2006 %A Stockwell, David R.B. %D 2006 %J Ecological Modelling %K methods Remotesensing Datamining Ecologicalnichemodeling WhyWhere Surrogatemodels predictivemodel %N 1-2 %P 188--196 %R DOI: 10.1016/j.ecolmodel.2005.05.029 %T Improving ecological niche models by data mining large environmental datasets for surrogate models %U http://www.sciencedirect.com/science/article/B6VBS-4GNTFYM-1/2/af4bd2e4a82010c65ea099fbd291e9aa %V 192 %X WhyWhere is a new ecological niche modeling (ENM) algorithm for mapping and explaining the distribution of species. The algorithm uses image processing methods to efficiently sift through large amounts of data to find the few variables that best predict species occurrence. The purpose of this paper is to describe and justify the main parameterizations and to show preliminary success at rapidly providing accurate, scalable, and simple ENMs. Preliminary results for six species of plants and animals in different regions indicate a significant (p<0.01) 14% increase in accuracy over the GARP algorithm using models with few, typically two, variables. The increase is attributed to access to additional data, particularly remotely sensed monthly versus annual climate averages. WhyWhere is also six times faster than GARP on large datasets. A data mining based approach with transparent access to remote data archives is a new paradigm for ENM, particularly suited to finding correlates in large databases of fine resolution surfaces. Software for WhyWhere is freely available, both as a service and in a desktop downloadable form from the web site http://biodi.sdsc.edu/ww_home.html.

@article{Stockwell2006, abstract = {WhyWhere is a new ecological niche modeling (ENM) algorithm for mapping and explaining the distribution of species. The algorithm uses image processing methods to efficiently sift through large amounts of data to find the few variables that best predict species occurrence. The purpose of this paper is to describe and justify the main parameterizations and to show preliminary success at rapidly providing accurate, scalable, and simple ENMs. Preliminary results for six species of plants and animals in different regions indicate a significant (p<0.01) 14% increase in accuracy over the GARP algorithm using models with few, typically two, variables. The increase is attributed to access to additional data, particularly remotely sensed monthly versus annual climate averages. WhyWhere is also six times faster than GARP on large datasets. A data mining based approach with transparent access to remote data archives is a new paradigm for ENM, particularly suited to finding correlates in large databases of fine resolution surfaces. Software for WhyWhere is freely available, both as a service and in a desktop downloadable form from the web site http://biodi.sdsc.edu/ww_home.html.}, added-at = {2010-01-14T17:51:28.000+0100}, author = {Stockwell, David R.B.}, biburl = {https://www.bibsonomy.org/bibtex/222e8b16dadca9dbf6fe311fd9054b31e/uvesco}, doi = {DOI: 10.1016/j.ecolmodel.2005.05.029}, file = {:Stockwell2006.pdf:PDF}, interhash = {2c74d28a1c0af6c62898f9ae99fba902}, intrahash = {22e8b16dadca9dbf6fe311fd9054b31e}, issn = {0304-3800}, journal = {Ecological Modelling}, keywords = {methods Remotesensing Datamining Ecologicalnichemodeling WhyWhere Surrogatemodels predictivemodel}, number = {1-2}, pages = {188--196}, timestamp = {2010-01-14T17:51:28.000+0100}, title = {Improving ecological niche models by data mining large environmental datasets for surrogate models}, url = {http://www.sciencedirect.com/science/article/B6VBS-4GNTFYM-1/2/af4bd2e4a82010c65ea099fbd291e9aa}, volume = 192, year = 2006 }