Forests can potentially support many species, but not all do. Understanding why forests support different numbers of species and how forest diversity can be threatened by climate change, land-cover change, and rising atmospheric CO2 requires data and modeling approaches that account the for complex interactions among species and with their environment. We must assimilate information derived from many sources, including field experiments, monitoring, and remote sensing. Current methods for prediction cannot take advantage of the disparate types of information currently available, and they necessarily ignore many of the important variables. They cannot be expected to yield useful predictions.
This project proposes to exploit new computational methods to incorporate the many types of information available and to assess the factors that affect biodiversity. To accomplish these goals the project involves combining powerful new techniques in computational statistics, algorithms, and data structures with extensive data on ecological interactions. We integrate these new tools to test the assumptions that diversity is maintained specific competitive relationships among species, disturbance, or a combination thereof. We use the same models to predict potential outcomes of current global change.
The intellectual merits of the proposed activity include new insight into the factors that control diversity in nature (as opposed to in simple models), which is a basic requirement for wise stewardship of forest resources and biotic diversity. Moreover, the technical advances, particularly the integration of modern computation with ecological process, will be of immediate application for a range of environmental issues.
The broader impacts of the proposed research will include understanding of forest management and conservation, carbon sequestration, responses to climate change and rising CO2, and invading species. Our research will also establish stronger links between ecology and computer science.
