This project will develop a new network that will bring together developers of simulation models used most widely in population ecology with the leaders of long-term studies on wildlife populations being impacted by habitat modification, landscape change, disease, climate change, and human intervention. The global environment is changing rapidly, and these changes have cascading impacts through local ecosystems, causing unprecedented loss of biodiversity and disruption of ecological processes on which natural and human systems depend. Understanding, predicting, and managing these impacts will require methods of analysis that integrate across traditionally separate fields of study. Simulation modeling has become a powerful means to study complex systems with interacting components that cannot be described by simple equations or tested with laboratory experiments, and simulation models have been embraced by population, community and landscape ecology, epidemiology, and evolutionary genetics, as well as resource management and climate research. Using a flexible approach called metamodels, simulation models can be linked so that they each represent specific processes impacting the system, while together they represent the interactions among processes acting at diverse spatial, temporal, and organizational scales. The network collaborators will make use of existing data on well-studied systems to develop methods and software tools to link models of different subsystems (such as dispersal, sociality, population dynamics, and landscape change), and test if these metamodel linkages better identify fundamental drivers of ecological systems, reveal important relationships not observed in single models, and ultimately result in more accurate predictions about complex systems.
This research coordination network will create new collaborations not only across fields of study, but also across academia, government, non-governmental research and conservation organizations, and business. The new tools developed in this project will enable interdisciplinary research far beyond participants in this project, provide methods and cases for educating students in interdisciplinary methods, and give decision-makers a powerful framework for integrating diverse information to solve complex problems.
