The science of ecology attracts a larger number of more diverse students when it focuses convincingly on addressing environmental problems. Ecological forecasting has the potential to capitalize on this desire because a forecasting approach can explicitly include the perspectives of stakeholders and because the iterative nature of the forecasting cycle allows students to understand the impacts of decisions on ecosystems and their services. Today, however, forecasting rarely serves as a framework for introducing ecological concepts early in ecological curricula, and graduate forecasting courses emphasize advanced computational and statistical methods. The Ecological Forecasting Initiative (EFI) is developing a systematic five year strategy to develop best practices for training in ecological forecasting. We consider how predictive forecasting might serve as a compelling gateway to the discipline of ecology and we asked what components would be required in an ecological curriculum with a forecasting orientation at both the undergraduate and graduate level. Discussion at this symposium will help broaden and enrich this discussion within EFI and beyond.
Results/Conclusions
The integrated nature of forecasting, with components of computation, diverse data sources, modeling, and decision-making potentially allow forecasting problems to be included in ecological curricula as team science exercises, where good forecasts rely on diverse strengths and perspectives. Ensuring that this opportunity expands the number and diversity of ecologists in the next generation requires that traditionally underrepresented groups are incorporated in the design of problems, tools, and curricula at early stages. Efforts by EFI researchers and others to produce standard tools and analysis will reduce analytical and computational barriers in the near future. It is also the case, however, that traditional quantitative training in ecology will have to change for forecasting to become a more standard approach in ecology. Developing and sharing modular online tools that use standardized data, for instance NEON data, will make forecasting problems more accessible and will enable forecasting components to be more easily incorporated into standard ecological curricula. We encourage audience members at this symposium to critique and improve our strategy for encouraging an applied and predictive component in ecological training.