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

• Published in: Oecologia Vol. 167; no. 3; pp. 599 - 611
• Main Authors: Zobitz, J. M., Desai, A. R., Moore, D. J. P., Chadwick, M. A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer 01-11-2011; Springer-Verlag; Springer Nature B.V
• Subjects: Analysis; Animal and plant ecology; Animal, plant and microbial ecology; Animals; Aquatic Organisms - growth & development; Aquatic Organisms - physiology; Atmospheric sciences; Biological and medical sciences; Biomedical and Life Sciences; Concepts; CONCEPTS, REVIEWS AND SYNTHESIS; Data collection; Data Interpretation, Statistical; Ecological modeling; Ecological models; Ecology; Ecology - methods; Ecology - trends; Ecosystem models; Ecosystems; Fundamental and applied biological sciences. Psychology; General aspects; Hexagenia limbata; Hydrology/Water Resources; Insecta; Insecta - growth & development; Insecta - physiology; Invertebrates; Life Sciences; Markov Chains; Markov processes; Mathematical independent variables; Modeling; Models, Biological; Models, Statistical; Monte Carlo Method; Mortality; Net ecosystem exchange; Parametric models; Plant Sciences; Population Dynamics; Respiration; Reviews and Syntheses; Technology application; Trees - growth & development; Trees - physiology; Ecological models; Markov Chain Monte Carlo; Aquatic insects; Data assimilation; NEE; Assimilation; Markov chain; Arthropoda; Insecta; Ecology; Models; Invertebrata
• ISSN: 0029-8549; 1432-1939
• DOI: 10.1007/s00442-011-2107-9

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.