LTREB: Using forecasting and long-term experiments to understand ecological dynamics under novel conditions

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Ecosystems and the services they provide are changing. This makes predictions for how systems will change crucial for decision making by land managers and policy makers. However, current capabilities for making ecological forecasts are limited. Making forecasts requires understanding how ecosystems will respond to changing conditions. Because ecosystems are governed by complex interactions among species and their environment, our knowledge from the past may provide limited information about the future as conditions change. Thus, it is critical to develop and assess our ability to make forecasts when novel conditions occur. For over 40 years, the Portal Project has been collecting data on mammals and plants as part of a long-term experiment in southeastern Arizona. Continuing data collection at this site provides a unique opportunity to (1) assess how the occurrence of novel conditions impact the ability to forecast the population sizes of plants and mammals and (2) determine the best methods to forecast changes in ecological systems. This project will support the growing field of ecological forecasting by providing a high-quality, openly available data source for other researchers. The research team will also develop forecasting competitions to engage the broader scientific community in forecasting efforts and produce online educational materials to support classes to teach the next generation of ecological forecasters.This research project will use the unique strengths of the Portal Project to improve ecological forecasting under novel conditions. Comparing the performance of forecasting approaches under novel conditions requires long-term data and novel environments. Over the past two decades, the climate at the Portal Project has become warmer and drier. This creates novel environmental conditions for species. Additionally, experiments at the site create novel combinations of species. Ongoing data collection will be used to assess: (1) if models with more ecological complexity perform better, (2) if data from experiments can improve forecasts, and (3) if forecasting models can handle rapid changes. This research will use an automated forecasting system that serves as a model for ecological forecasting. The research requires ongoing data collection to test forecasts and to provide information on ecological changes as species and the environment change.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.