Evaluating and applying emerging ecological niche modelling techniques to study the changing distribution of Mexican cloud forests.
Understanding the geographic distribution of species and how species distributions will change in response to various drivers of environmental change is one of the fundamental problems of biodiversity science. In recent years, ecological niche modelling has been used to forecast effects of global climate change on species’ geographic distributions. These forecasting processes require diverse operations on heterogeneous datasets (primary biodiversity data, raster climate data, raster land use data, raster vegetation index data, and climate change scenarios) and application of a variety of computational steps (database management, quality control, ecological niche modeling, place-prioritization algorithms) on several platforms to obtain the final product.
This burgeoning field has developed largely without a theoretical framework or a rigorous baseline of known performance evaluation. This project will advance the conceptual basis of this field by developing a rigorous methodology to evaluate different approaches by designing and constructing virtual environments where all relevant variables can be controlled. Current algorithms will be tested and improved by extensive testing carried out in these virtual environments. If needed, new algorithms may be developed.
The project will also develop and implement scientific workflows to carry out these complex analyses in an accessible, user-friendly environment. A first application will be to predict effects of climate change on the biodiversity of Mexican cloud forests. This project will be the first to apply large databases of primary biodiversity data and cutting-edge analytical methods to this threatened and endangered ecosystem. More importantly, it will develop both theoretical and computational frameworks for broad application of this overall approach. The result will be an analytical environment that supports diverse forecasting applications for complex biodiversity phenomena.
- Jorge Soberón (Biodiversity Research Center, The University of Kansas)
- A. Townsend Peterson (Biodiversity Research Center, The University of Kansas)
- Raúl Jiménez (Comisión Nacional para el Uso y Conocimiento de la Biodiversidad (CONABIO), México)
- Isabel M D Rosa, Drew Purves, Carlos Souza Jr, and Robert M Ewers, Predictive Modelling of Contagious Deforestation in the Brazilian Amazon, in PLOS One, PLoS, October 2013.
- Lucas Joppa, Piero Visconti, C.N. Jenkins, and S.L. Pimm, Achieving the Convention on Biological Diversity’s Goals for Plant Conservation, in Science, vol. 341, pp. 1100-1103, American Association for the Advancement of Science, September 2013.