Species Distribution Modeling

Expert Services


For the last two decades, resource managers have been striving for ways to accomplish earlier, better, and more effective environmental decision making. Species Distribution Modeling (SDM) is an innovative GIS-based method used to produce predictive maps of where species are likely to occur and likely not to occur. NatureServe’s expertise in using its core data to model the predicted distribution of species provides game-changing tools for planning and assessment.


Traditional datasets that are comprised only of the known locations of species have limits from a planning perspective because they do not predict other places where threatened resources are likely to occur, and as significant, through probability analyses, areas which are not potential habitat for any given species. The NatureServe network can readily develop these models for species in any geographic area and across a species range, enabling greater focus and specificity with regard to avoidance, minimization, and mitigation.

Features & Benefits

Conserving species and habitats depends in part on knowing where they occur. For more than two centuries biologists have conducted field inventories to map the distribution of plants and animals, yet our understanding of the distribution of most species are still incomplete, due to the challenges of field work. Today, we can say with certainty where a particular species has been found, but we need to know where else it is likely to occur.

The development of high-speed computers, geographic mapping software, and sophisticated analytical techniques have matured and reached scientific acceptance in the last five years allowing the production of spatially explicit species distribution models. By taking known mapped locations of species populations managed by the NatureServe network and correlating that with a wide set of existing environmental data—landscape position, vegetation and habitat characteristics such as slope, aspect, and many others, species distribution models can now efficiently be produced to meet primary needs, including:

  • aid in addressing impacts of development and habitat alteration on species,
  • aid in recovery of listed species by helping focus surveys and identify potential sites for protection, restoration, and reintroduction,
  • correlate species distributions with climate and other landscape variables, allowing for modeling potential climate change impacts, and
  • serve as a basis for development of more advanced decision-support systems and other tools and applications to support conservation.