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.

Enhanced soil respiration in response to global warming may substantially increase atmospheric CO2 concentrations above the anthropogenic contribution, depending on the mechanisms underlying the temperature sensitivity of soil respiration. Here, we compared short-term and seasonal responses of soil respiration to a shifting thermal environment and variable substrate availability via laboratory incubations. To analyze the data from incubations, we implemented a novel process-based model of soil respiration in a hierarchical Bayesian framework. Our process model combined a Michaelis-Menten-type equation of substrate availability and microbial biomass with an Arrhenius-type nonlinear temperature response function. We tested the competing hypotheses that apparent thermal acclimation of soil respiration can be explained by depletion of labile substrates in warmed soils, or that physiological acclimation reduces respiration rates. We demonstrated that short-term apparent acclimation can be induced by substrate depletion, but that decreasing microbial biomass carbon (MBC) is also important, and lower MBC at warmer temperatures is likely due to decreased carbon-use efficiency (CUE). Observed seasonal acclimation of soil respiration was associated with higher CUE and lower basal respiration for summer- vs. winter-collected soils. Whether the observed short-term decrease in CUE or the seasonal acclimation of CUE with increased temperatures dominates the response to long-term warming will have important consequences for soil organic carbon storage.

Stable isotopes are valuable tools for partitioning the components contributing to ecological processes of interest, such as animal diets and trophic interactions, plant resource use, ecosystem gas fluxes, streamflow, and many more. Stable isotope data are often analyzed with simple linear mixing (SLM) models to partition the contributions of different sources, but SLM models cannot incorporate a mechanistic understanding of the underlying processes and do not accommodate additional data associated with these processes (e.g., environmental covariates, flux data, gut contents). Thus, SLM models lack predictive ability. We describe a process-based mixing (PBM) model approach for integrating stable isotopes, other data sources, and process models to partition different sources or process components. This is accomplished via a hierarchical Bayesian framework that quantifies multiple sources of uncertainty and enables the incorporation of process models and prior information to help constrain the source-specific proportional contributions, thereby potentially avoiding identifiability issues that plague SLM models applied to "too many" sources. We discuss the application of the PBM model framework to three diverse examples: temporal and spatial partitioning of streamflow, estimation of plant rooting profiles and water uptake profiles (or water sources) with extension to partitioning soil and ecosystem CO2 fluxes, and reconstructing animal diets. These examples illustrate the advantages of the PBM modeling approach, which facilitates incorporation of ecological theory and diverse sources of information into the mixing model framework, thus enabling one to partition key process components across time and space.

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