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

Outline 1.Historical Perspective 2.Present opportunities 3.Future prospects

Historical Perspective

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

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

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

Simple ecological models (1800s-1950s) 1. Growth models Logistic growth equation – Pierre Verhulst Competition model – Lotka-Volterra model 1925, 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

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

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

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

Systems analysis 1.First described by Heraclitus in 6 th century BC 2.Active research tools in 1930s-40s 3.Used in ecology in 1950s–60s by Odum, Watt, and many others. Holistic analysis on structure and behavior of a system as a whole.

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

Simulation model (1960s- present) 1.Forrester, J.W Industry Dynamics 2.De Wit in Netherlands, 1960s – 90s 3.Applications in ecology 1960s – pres 4.Example: CENTURY Uses 1.Synthesis and integration of data 2.Predicting the behavior of ecosystems 3.Hypothesis generation for study design 4.Policy making.

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

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

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

Techniques of Optimization in Data-model Assimilation Stochastic inversion 1. Bayesian inversion – Thomas Beyes (1701 – 1761) 2. Markov Chain Monte Carlo – Metropolis-Hastings (1950s) 3. Simulated annealing (Kirkpatrick et al. 1983) 4. Genetic algorithms (Goldberg 1989) Deterministic inversion 1.Steepest descending 2.Newton method –Isaac Newton (1711) 3.Newton-Gauss method 4.Levenburg-Marquardt algorithm (1944, 1963)

Use of both process thinking and information contained in data towards a global synthesis. 1. Parameter estimation 2. Test of model structure 3. Uncertainty analysis 4. Evaluation of sampling strategies 5. Forecasting Potential Uses of the Data-model fusion

Present Opportunities

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. FLUXNET

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

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

NEON

Transformational research for a data-rich era CharacteristicsData-poor eradata-rich era Activities Data collectionData processing Major effortMeasurements Theory development and test InformaticsSpreadsheetEco-informatics ObjectivesDiscoveryForecasting MotivesCuriosity-drivenDecision making Service to societyLong-term Real-time

Future prospects

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

Future research 1.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. 2.Streamline real-time data collection, QA/QC, and data-model assimilation and data products. 3.Test theory for model development. 4.Support decision making processes

Summery 1.Data and model are two foundational approaches to scientific inquiry about natural world. 2.Data-model assimilation combines the bests from both approaches 3.As we enter a data-rich era, data-model assimilation becomes an essential tool of ecological research. 4.Data-model assimilation ultimately help ecological forecasting to best serve the society