OPUS: A Computational Theory of Biodiversity

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

Biodiversity can be defined as the patterns of distribution of species, genes, and ecosystems on the planet. It is amply documented that the activities of human societies are substantially increasing the extinction rate of species, the loss of genetic variation, and the degradation of ecosystems. This loss imperils many of the services that biodiversity offers such as pollination, pest control, cleansing of water, promoting soil health, and providing resources for innovation in industry and health. Thus, forecasting how the distribution of species change through time is of urgent and critical importance to conserve the remaining biodiversity on this planet. This project will make important contributions to this goal by synthesizing publicly-available data and decades of previous research to develop novel computational models that will realistically predict changes in the distributions of species, communities, and ecosystems. Applications of these models will provide insight into the expansion of invasive species, emergence of human and agricultural diseases, and the impacts of climate change on threatened and endangered species. Results of this project include visual and dynamic maps that will form interactive displays at the University of Kansas Natural History Museum. An important training component is the involvement of a graduate student in the development of the dynamic mathematical models of species distributions as well as the writing of the book and software. The synthesis of decades of research and data will culminate in a book and software that will be accessible to the evolution and ecology research community so that the new models developed in this project can be used broadly and extensively across many species and ecological communities around the world. Currently, most models of species distributions are static and based on presence-absence data; this project will incorporate additional data sources such physiology and species interactions to generate dynamic distribution models for those species. Furthermore, this project will use these models from multiple species to represent ecological changes of whole communities and environments through time and to predict biodiversity patterns over entire landscapes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.