Collaborative Proposal: MSB-ENSA: The Near-term Ecological Forecasting Initiative

点击翻译按钮获取中文摘要

Living systems are changing worldwide and critical decisions that affect their health and sustainability are being made every day. In the face of climate change and other environmental challenges, society can no longer rely solely on past experience to understand and manage the living world. This award asks the question, ?What would it take to forecast ecological processes the same way we forecast the weather?? This project will development an operational ecological forecasting capability similar to weather forecasting that uses an iterative cycle between making forecasts, performing analyses, and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for building a forecast capacity, and also a crucial part of any decision making under high uncertainty. In addition to making ecology more relevant to management, near-term forecasts routinely compare specific, quantitative predictions to new data, which is one of the strongest tests of any scientific theory. This project will generate near-term forecasts that leverage ecological data collected by the National Ecological Observatory Network and spanning a wide range of themes: leaf phenology, land carbon and energy fluxes, tick-borne disease incidence, small-mammal populations, aquatic productivity, and soil microbial diversity and function. This broad, comparative approach will be used to address cross-cutting questions about the nature of predictability in ecology and develop an overarching body of forecasting theory and methods. The Near-term Ecological Forecasting Initiative (NEFI) will advance ecological knowledge at three levels: (1) overarching across-theme hypotheses about the predictability of ecological systems; (2) pressing within-theme questions about what drives process and predictability; and (3) advancing the tools and techniques that will enable an iterative approach to quantitative hypothesis testing. The overarching hypotheses of this project are that: (1) ecological predictability is more driven by processes error than initial condition error; (2) there are consistent patterns in the sources of uncertainty across themes; (3) across themes, spatial and temporal autocorrelation are positively correlated; and (4) spatial and temporal autocorrelation are positively correlated with limits of predictability. Overall, the answers to these questions address to what extent there are general patterns to ecological predictability, which would advance both our basic understanding of ecological processes and constrain the practical problem of making forecasts.The expected outcomes of NEFI are to: (1) Disseminate data products and predictions that benefit society; (2) Develop new tools and cyberinfrastructure that enhances research and education; and (3) to promote teaching, training, and learning. Specific NEFI forecasts, such as tick-borne disease risk, aquatic blooms, carbon sequestration, and leaf phenology, are of direct relevance to society. Forecasts will be made available via open cyberinfrastructure that disseminates forecasts to the public and allows other ecologists to contribute new forecasts. To produce these forecasts, NEFI will develop an open-source statistical package, ecoforecastR, which will advance the tools and techniques beyond what is currently used by the community. Finally, in addition to the graduate students directly mentored through the project, NEFI will run an annual summer course on ecological forecasting that will train the next generation of ecologists.