EAGER: Making code-based analyses widely accessible for modeling species niches and distributions

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Environmental biologists model both where organisms live, their distribution in space (biogeography) and how they interact with the local environment, filling a particular niche. To do this modeling requires data from a number of sources and the use of multiple analytical packages: putting these together in useful workflows requires specialized technical abilities. This proposal will create a software application, called 'Wallace', that brings together the data and analytical components through a graphical interface to make selection of components easier, saves the workflow so others can rerun it, and provides guidance for the best settings for the analyses. It will also provide interactive maps, tables and graphs to explore the results. The application has been designed to be easily extended, and the software will be managed as an open-source, freely available resource through Github. Among other advances, results from using the software will lead to better estimates in conservation biology, invasive species, and the impact of climate change. A substantial divide exists in environmental biology between researchers with and without strong coding skills: the proposed activities aims to close that gap, using an innovative approach to software development that could transform the field. In the initial phase, The project will release the software application 'Wallace', to enable best computational modeling practices for the burgeoning field of niche/distributional modeling. This software is a modular web application (that also can be run locally) offering customizable workflows through a Graphical User Interface (GUI). It will be built using the recently developed R package shiny, which will allow easy extensibility to the many cutting-edge methods now being produced in the field, via the addition of R packages. Specifically, Wallace will: 1) harness data either from online databases or input by the user; 2) assemble a variety of tools for building and evaluating models; 3) give guidance text for critical conceptual and methodological issues; 4) feature interactive maps, tables, and graphs to view and explore data and model predictions; 5) offer results for download in multiple forms after each stage of the analysis; and 6) provide documented and executable code for rerunning the entire analysis at the end of a user session. Its modular nature will facilitate later addition of new elements by other researchers as the software matures.