In recent years a burgeoning number of statistical and related methods have been used with mapped biological and environmental data in order to model, or, in some way, spatially interpolate species distributions over large spatial extents. Maps of actual or potential species distributions or habitat suitability are required for many aspects of environmental research, resource management, and conservation planning. These applications include biodiversity assessment, biological reserve design, habitat management and restoration, species and habitat conservation plans, population viability analysis, environmental risk assessment, invasive species management, community and ecosystem modeling, and predicting the effects of global environmental change on species and ecosystems. Lack of knowledge among students and researchers is an important constrain in developing, accessing and interpreting Species Distribution Models. With the availability of GIS data and new spatial tools, biologists can now readily obtain environmental data for thousands of localities and hundreds of species across the globe, permitting them to more rigorously explore the underlying ecological and evolutionary causes of bio-geographic patterns.

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Therefore, a workshop on SDM will greatly benefit researchers to learn different SDM techniques and also to develop an understanding about theories and assumptions associated with SDMs. The first chapter of this workshop, organized last year, was focused on the use of MaxEnt algorithm along with a glimpse of using R (a free software for statistical computing with the powerful scientific programming environment) in SDM. With the rapid increase of R applications in the field of natural sciences, it has powerful and extensive data analysis packages related to SDM. The second chapter of this workshop is therefore designed to emphasize on using R in species distribution modelling, highlight the state of the art techniques in this domain of research along with naturalizing the participants with the MaxEnt environment.