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A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC)

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Abstract

Data assimilation, or the fusion of a mathematical model with ecological data, is rapidly expanding knowledge of ecological systems across multiple spatial and temporal scales. As the amount of ecological data available to a broader audience increases, quantitative proficiency with data assimilation tools and techniques will be an essential skill for ecological analysis in this data-rich era. We provide a data assimilation primer for the novice user by (1) reviewing data assimilation terminology and methodology, (2) showcasing a variety of data assimilation studies across the ecological, environmental, and atmospheric sciences with the aim of gaining an understanding of potential applications of data assimilation, and (3) applying data assimilation in specific ecological examples to determine the components of net ecosystem carbon uptake in a forest and also the population dynamics of the mayfly (Hexagenia limbata, Serville). The review and examples are then used to provide guiding principles to newly proficient data assimilation practitioners.

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Acknowledgments

The authors would like to thank T. Quaife for helpful discussions, students of the Annual Summer Course in Flux Measurements and Modeling (NSF 0634649), and anonymous reviewers. ARD acknowledges funding support from Department of Energy (DOE) Office of Biological and Environmental Research (BER) National Institute for Climatic Change Research (NICCR) Midwestern Region Subagreement 050516Z19.

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Author notes

  1. D. J. P. Moore

    Present address: School of Natural Resources and Environment, University of Arizona, 1955 E. Sixth Street, Suite 205, Tucson, AZ, 85721, USA

Authors and Affiliations

  1. Department of Mathematics, Augsburg College, 2211 Riverside Avenue, Minneapolis, MN, 55454, USA

    J. M. Zobitz

  2. Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, 1225 W Dayton St, Madison, WI, 53706, USA

    A. R. Desai

  3. Department of Geography, King’s College London, Strand, London, WC2R 2LS, UK

    D. J. P. Moore & M. A. Chadwick

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Additional information

Communicated by Russell Monson.

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Supplementary material 1 (PDF 287 kb)

The ESM for this paper includes helpful “Rules of Thumb” for data assimilation, a glossary of data assimilation terminology, a table outlining different data assimilation studies, additional results to those described above, and all data and codes used to generate the results. These data and programs are also available at: http://www.augsburg.edu/home/math/faculty/zobitz/dataAssimilation.html.

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Zobitz, J.M., Desai, A.R., Moore, D.J.P. et al. A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC). Oecologia 167, 599–611 (2011). https://doi.org/10.1007/s00442-011-2107-9

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