The environmental sciences have progressively found themselves thrust from their humble roots in natural history into the role of detecting, quantifying, and predicting the interactions between humankind and our natural environment. We face a future where there is clear and growing demand for quantitative ecological forecasts with accurate assessments of uncertainty at the local, national, and global level. One of the primary goals of our work is to produce ecological forecasts by combining innovative ecological models with cutting-edge statistical and computational techniques and integrating diverse sources of data across many spatial and temporal scales. Forecasting is not merely an exercise in modern information technology, but requires tackling a number of basic research questions. At the forefront of these is the need to go beyond studying individual sites in isolation in order to understand the generalities across ecological systems. Basic science questions are what ultimately that drive our research: how do species coexist?; what are the relative contributions of biotic interactions, abiotic factors, and disturbance in structuring ecosystems?; and to what extent are ecosystem dynamics predictable versus determined by individual history and chance events? We are interested in understanding the universal constraints on vegetation dynamics through the integration of cross-site studies and focused field campaigns with cutting-edge models and modern statistical techniques. Overall our research is focused on the interacting roles of environmental heterogeneity, disturbance, and climate change in structuring vegetation dynamics.
Current projects are split between those focused on climate change responses versus those on novel biofuel crops. Both share many of the same questions about carbon fluxes and impacts on ecosystems services and biodiversity, and both use many of the same tools. The longest running work in the lab has focused on forest dynamics in the eastern and central U.S. at the stand, landscape, and regional scales, while the newest project in the Alaskan tundra looks at vegetation-fire-climate feedbacks. In our biofuels work we look at the suitability of different woody and perennial grass biofuel crops, their vulnerability to climate variability, their impacts on carbon storage and the water cycle, and the potential land use/land cover changes of biofuel expansion. Past projects have also involved work in Costa Rica, Australia, and the Pacific Northwest.
Dietze et al. 2014. A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes. Journal of Geophysical Research Biogeosciences Online Early
Niu et al. The role of data assimilation in predictive ecology. Ecosphere Accepted
Zaehle et al. 2014. Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies. New Phytologist DOI: 10.1111/nph.12697 Early View pdf
PalEON summer course "Assimilating Long Term Data into Ecosystem Models" accepting applications link
NASA project funded for hyperspectral / ED2 radiative transfer model inversion. Congrats to Shawn Serbin (lead PI and lab alum). The lab is looking for one graduate student to work on this project
Brown Dog project funded ($10.5M) to chase the long-tail of uncurated scientific data link. The lab is looking for two graduate students to work on this project.
Dietze et al. 2014. Nonstructural Carbon in Woody Plants. Annual Reviews in Plant Biology Link
European COST activity funded, "Towards robust PROjections of European FOrests UNDer climate change (PROFOUND)"
Feng, X. and M. Dietze. 2013. Scale-dependence in the effects of leaf economic traits on photosynthesis: Bayesian parameterization of photosynthesis models. New Phytologist in press pdf
Urban et al. 2013. Classifying del13C values of individual grains of C3 and C4 grass pollen using a hierarchical Bayesian approach. Geochimica et Cosmochimica Acta. in press pdf
LeBauer et al. 2013 Ecological Monographs paper recommending in two F1000 reviews
Dietze M. 2013. Gaps in knowledge and data driving uncertainty in models of photosynthesis. DOI: 10.1007/s11120-013-9836-z online early pdf
Dietze, M.C., D. LeBauer, R. Kooper. 2013. On improving the communication between models and data. Plant, Cell, and Environment in press pdf