Data-Model Assimilation in Ecology History, present, and future

1. Data-Model Assimilation in Ecology History, present, and future Yiqi Luo University of Oklahoma

2. Outline • Historical Perspective • Present opportunities • Future prospects

3. Historical Perspective

4. Data-model Assimilation Process thinking Synthesis and prediction Information contained in data

5. Processes thinking Data Approaches to scientific research Experiment (observation) Model (Theory) Theory delineates possibilities Empirical studies discriminate the actualities Robert May 1981

6. Simple model Approaches to scientific research Model – Theory Experiment Processes thinking Data

7. Simple ecological models (1800s-1950s) 1. Growth models Logistic growth equation – Pierre Verhulst 1838 2. Competition model – Lotka-Volterra model 1925,1926 3. Predation model Merits Generalizations that sum up many measurements of attribute and, within limits, can be used for predictions. Weakness No much information on mechanisms or processes

8. Probability Statistic analysis Approaches to scientific research Model – Theory Experiment Processes thinking Data Simple model

9. Statistical analysis (1600s-) 1654 – Pascal developed mathematics of probability 1805 – A-M Legendre – Least square method 1877-1889 – F. Galton – regression and correlation 1919 – R.A. Fisher – ANOVA 1960s- Ecology literature Analysis, interpretation, and presentation of masses of numerical data.

10. Approaches to scientific research Model – Theory Experiment Processes thinking Data Simple model Systems analysis Probability Statistic analysis

11. Systems analysis • First described by Heraclitus in 6th century BC • Active research tools in 1930s-40s • Used in ecology in 1950s–60s by Odum, Watt, and many others. Holistic analysis on structure and behavior of a system as a whole.

12. Approaches to scientific research Model – Theory Experiment Processes thinking Data Simple model Systems analysis Probability Statistic analysis Simulation model

13. Simulation model(1960s- present) • Forrester, J.W. 1961 Industry Dynamics • De Wit in Netherlands, 1960s – 90s • Applications in ecology 1960s – pres • Example: CENTURY • Uses • Synthesis and integration of data • Predicting the behavior of ecosystems • Hypothesis generation for study design • Policy making.

14. Simulation model (cont.) • Challenges • Low confidence on model output • Model validation and testing against data • Transparency and amenability to analysis.

15. Approaches to scientific research Model – Theory Experiment Processes thinking Data Simple model Systems analysis Probability Statistic analysis Simulation model Baysian analysis Data-model assimilation

16. Simulation model vs. data-model assimilation Data-model fusion Simulation modeling Multiple Datasets Inverse modeling Parameter estimates from literature Inverse model Simulation model Forward modeling Simulation (forward) model Model prediction Model predictions

17. Techniques of Optimizationin Data-model Assimilation • Deterministic inversion • Steepest descending • Newton method –Isaac Newton (1711) • Newton-Gauss method • Levenburg-Marquardt algorithm (1944, 1963) • Stochastic inversion • Bayesian inversion – Thomas Beyes (1701 – 1761) • Markov Chain Monte Carlo – Metropolis-Hastings (1950s) • Simulated annealing (Kirkpatrick et al. 1983) • Genetic algorithms (Goldberg 1989)

18. Potential Uses of the Data-model fusion • Use of both process thinking and information contained in data towards a global synthesis. • Parameter estimation • Test of model structure • Uncertainty analysis • Evaluation of sampling strategies • Forecasting

19. Present Opportunities

20. FLUXNET A worldwide network with over 400 tower sites operating on a long-term and continuous basis, supplemented with data on site vegetation, soil, hydrologic, and meteorological characteristics at the tower sites.

21. TERACC A worldwide network with over 100 manipulative experimental sites to study impacts of global change factors on ecosystem processes.

22. Long Term Ecological Research (LTER) Network LTER Network established in 1980, has 26 sites, and involves more than 1800 scientists and students investigating ecological processes over long temporal and broad spatial scales. Synthesis across sites is one of the major challenges for LTER

23. NEON

24. Transformational research for a data-rich era CharacteristicsData-poor eradata-rich era Activities Data collection Data processing Major effort Measurements Theory development and test Informatics Spreadsheet Eco-informatics Objectives Discovery Forecasting Motives Curiosity-driven Decision making Service to society Long-term Real-time

25. Future prospects

26. NEON and other sensor networks Theory Real-time data strings ecological models Eco-informatics Data-model fusion Ecological forecasting Preparation for catastrophe Resource management Decision making

27. Future research Eco-informatics is not only about acquisition, analysis and synthesis, and dissemination of data and metadata but also include model assimilation to generate data products. Streamline real-time data collection, QA/QC, and data-model assimilation and data products. Test theory for model development. Support decision making processes

28. Summery Data and model are two foundational approaches to scientific inquiry about natural world. Data-model assimilation combines the bests from both approaches As we enter a data-rich era, data-model assimilation becomes an essential tool of ecological research. Data-model assimilation ultimately help ecological forecasting to best serve the society