Lifemapper's approach to Species Distribution Modeling
To create species distribution maps we first need to know the locations where the species has been found. We refer to these locations as ‘occurrence’ data – they are the recorded occurrences of a species. Lifemapper gets its occurrence data from the Global Biodiversity Information Facility (GBIF). GBIF gathers collection and observation data from museums all over the world, then Lifemapper groups these data by species.
Once we know where a species occurs we determine the environmental conditions at the time and place it was found to create a mathematical formula based on these environmental values. Most of our occurrence points were collected in the last 200 years, so we use climate conditions and approximate them using aggregated observed data from 1960-2000 from the Climate Research Unit of the University of East Angli and WorldClim.
If we apply this mathematical formula to the environmental conditions observed at points where the species has not been recorded, we can map not only where the species might live now, but also indicate places where the species has never been but might thrive if it were introduced. This gives us a projection of potential current day distribution based on observed conditions.
When using climate data for environmental conditions, we can apply the formula to future climate scenarios predicted by Global Climate Models (GCMs). Future scenarios are defined by the Intergovernmental Panel on Climate Change (IPCC) and explore potential trends in population, economic growth, emissions, energy use, and resource availability. Lifemapper uses climate data modeled by the Met Office Hadley Centre, and the National Institution for Environmental Studies NIES(NIES) for each of 3 different future scenarios.
An algorithm creates the mathematical formula defining the relationship between species occurrences and the environmental conditions at those locations. Lifemapper makes the following algorithms available:
- Artificial Neural Network
- Bioclimatic Envelope Algorithm
- Climate Space Model – Broken Stick Implementation
- Environmental Distance
- GARP – DesktopGARP Implementation
- GARP with Best Subsets – DesktopGARP Implementation
- GARP – openModeller Implementation
- GARP with Best Subsets – openModeller Implementation
- SVM (Support Vector Machines)