## 1 Introduction  
Predicting species current and potential distributions is critical to the effective management of invasive species [1], conservation of threatened and endangered species [2; 3; 4], and protection of native species and habitats [5]. Accurate predictive models can guide field surveys [6; 7], define monitoring priorities [8], and help direct conservation and restoration efforts at local, regional, and national scales [9].  
Over the past few decades, statistical and technical capabilities have progressed to a level where we now have a variety of techniques for predicting the potential distribution of a given species [10; 11; 12; 13]. These techniques use field observations of a species (_i.e._, presence-absence data or presence-only data) as a dependent variable, while independent or predictive variables represent environmental conditions (e.g., elevation, minimum temperature, mean monthly precipitation). Methods such as Maximum Entropy modeling [14] and the Genetic Algorithm for Rule-set Prediction [15] require only presence locations of a species as the dependent variable. Model algorithms are designed to analyze the statistical relationships between the dependent variable and independent variables and create geospatial representations of where a species is likely to occur and where it is likely absent. To date, these tools and methods have only been available to a small audience of researchers with the time and resources to bring together the data, the software, and acquire the knowledge to run the software required to create models.  
With today's computer hardware, computer software, and the Internet, we can bring modeling capabilities to a much wider audience, and strengthening the capacity of resource managers and citizen scientists while providing learning opportunities for K-12 teachers, students and young scientists. Advances in computer hardware and software have made it possible to setup and maintain computer servers that store large geospatial datasets, perform complex analyses on demand, and provide access to the data and analysis capabilities through a website [16]. Commercial and open source software can be assembled with custom programming into a system that is robust, secure, and reliable. The Internet provides access to web pages from anywhere in the world through the World-Wide-Web. Through web scripting we can provide access to analysis capabilities in a secure yet easy to use interface.  
There are many websites that allow users access to species occurrences records [17; 18]. There are also several systems that are beginning to provide predicted surfaces of potential species distributions. The Global Biodiversity Information Facility includes the ability to create world-wide maps of potential species distributions based on the openModeler software package [19]. Different spatial model algorithms are available for use with openModeler, generally producing world-wide maps at fairly coarse resolution, but with limited means to assess model uncertainty at various scales and levels of resolution [20]. LifeMapper is another website which includes models from BIOLIM and GARP [21]. What is missing is a system that provides the ability for any web user to collect field data, integrate it with other data, run distribution models against environmental layers, create predictive distribution maps, and do so at a variety of spatial scales. Our objective was to create such a system and make it available over the World-Wide-Web. The target audience for this system includes managers, citizen scientists, and students who do not have the expertise, resources, or time to develop models that are available to research scientists immersed in modeling.