Ecological forecasting and data assimilation in a data-rich era

• Published in: Ecological applications Vol. 21; no. 5; pp. 1429 - 1442
• Main Authors: Luo, Yiqi, Ogle, Kiona, Tucker, Colin, Fei, Shenfeng, Gao, Chao, LaDeau, Shannon, Clark, James S, Schimel, David S
• Format: Journal Article
• Language: English
• Published: United States Ecological Society of America 01-07-2011
• Subjects: Applied ecology; Climate models; Computer Simulation; data assimilation; Data Assimilation for Ecological Forecasting; data-–model fusion; ecological forecasting; Ecological modeling; Ecology; Ecology - methods; Ecology - trends; Ecosystem models; Ecosystems; Forecasting - methods; Forecasting models; inverse analysis; Modeling; Models, Theoretical; optimization; Parametric models; predictions; prognosis; projections; Time Factors; Weather forecasting
• ISSN: 1051-0761; 1939-5582
• DOI: 10.1890/09-1275.1

Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible.
This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.