Conserving species and habitats depends in large part on knowing where they occur. For more than two centuries biologists have conducted field inventories to map the distribution of plants and animals. However, the full distributions of species are still often unknown, as field inventories cannot cover the entire landscape. Fortunately, recent advances in statistics and computing now allow for comprehensive mapping of species habitat using an approach called species distribution modeling (SDM).
SDM is an innovative GIS-based method that combines species observation data with environmental predictors to better map habitat. Outcomes include maps of habitat suitability (from low to high) across the landscape. In areas of low suitability, confidence that the species is not present is high, while areas of high suitability can guide priorities for survey, protective measures, and restoration.
For over a decade, NatureServe and Natureserve member programs have used SDM to produce better maps of species habitat, using our extensive field inventory data to guide model development within particular states or for particular species. More recently, NatureServe network Modeling Centers have been working together to develop best practices for distribution modeling, generate models that cross jurisdictions, and develop a strategy to provide improved distribution maps for all at-risk species. NatureServe’s expertise in using its core data to model the predicted distribution of species provides game-changing tools for planning and assessment.
Without consistent, predictable, up-to-date, and scale-appropriate information to guide species conservation decisions, conservation efforts cannot be easily targeted where they will confer the greatest benefit. At the same time, a lack of knowledge about where species are unlikely to occur can result in resources being diverted to analyzing potential impacts to at-risk species that may never occur on the ground. By providing better information on where at-risk and regulated species are likely to be found, SDMs provide cost-savings to government and industry while benefiting biodiversity. These benefits include:
Greater efficiency and less uncertainty in implementation of the Endangered Species Act (ESA). More precise habitat maps can prevent the need for costly ESA consultations by pinpointing areas where impacts to endangered species are unlikely, increasing trust from the regulated community and reducing litigation.
Prevention of unnecessary species listings via increased understanding of habitat availability, direction of field surveys to locate new populations, and guidance for siting of management activities.
Greater efficiency in recovery efforts through better knowledge of species habitat needs and better identification of project areas given current and anticipated future (e.g. climate-driven) suitability.
Better targeting of conservation initiatives including net conservation benefit projects, land acquisitions, species management activities, and conservation planning applications.
A sound scientific basis for inventory and monitoring, enabling scientists to find new populations and providing the foundation for development of defensible and efficient monitoring protocols.
A sound scientific basis for adaptation planning; because SDMs can be modified to represent habitat suitability under changed conditions or into previously unoccupied areas, they allow scientists and managers to anticipate species response to natural or anthropogenic environmental change.
SDMs produced by the NatureServe network can provide conservation practitioners with the data and knowledge to support effective management. This includes:
Maps of habitat suitability (from low to high) across the landscape. In areas of low suitability, confidence that the species is not present is high, while areas of high suitability can guide priorities for survey, protective measures, and restoration.
Thresholded habitat maps. Created from modeled probabilities based on scientific standards and user-defined risk tolerance, habitat maps can be tailored to regulatory needs.
Information about species response to environmental factors. Distribution modeling can be used to identify which environmental factors are correlated to species occurrence, deepening our understanding of species biology and threats. Projecting models into new environments or climates enables exploration of potential response to changing environmental conditions.
Guidance on applications to decision-making. Interpretative materials will be provided with each model to guide decision-makers in appropriate data usage.
We strive to produce transparent and repeatable models informed by the vast species expertise of our network biologists, occurrence data collected over decades of inventory, and the collective modeling expertise of spatial analysts in over a dozen Modeling Centers.