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

predictionInformation 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 thinkingSimple model

Simple ecological models (1800s-1950s)

1. Growth modelsLogistic growth equation – Pierre Verhulst 1838

2. Competition model – Lotka-Volterra model 1925,19263. Predation model

MeritsGeneralizations that sum up many measurements of attribute and, within limits, can be used for predictions.

WeaknessNo much information on mechanisms or processes

Approaches to scientific research

Experiment Model – Theory

Data Processes thinkingSimple model

Statistic analysis

Probability

Statistical analysis (1600s-)

1654 – Pascal developed mathematics of probability1805 – A-M Legendre – Least square method1877-1889 – F. Galton – regression and correlation1919 – R.A. Fisher – ANOVA1960s- Ecology literature

Analysis, interpretation, and presentation of masses of numerical data.

Approaches to scientific research

Experiment Model – Theory

Data Processes thinkingSimple model

Statistic analysis

Systems analysis

Probability

Systems analysis

1. First described by Heraclitus in 6th century BC

2. Active research tools in 1930s-40s3. 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 thinkingSimple model

Statistic analysis

Systems analysis

Simulation model

Probability

Simulation model(1960s- present)

1. Forrester, J.W. 1961 Industry Dynamics2. De Wit in Netherlands, 1960s – 90s3. Applications in ecology 1960s – pres4. Example: CENTURY

Uses1. Synthesis and integration of data 2. Predicting the behavior of ecosystems3. Hypothesis generation for study design4. 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 ProcessesthinkingSimple

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

Inversemodeling

Forwardmodeling

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 descending2. Newton method –Isaac Newton (1711)3. Newton-Gauss method4. Levenburg-Marquardt algorithm (1944, 1963)

Use of both process thinking and information contained in data towards a global synthesis.

1. Parameter estimation2. Test of model structure3. 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

Characteristics Data-poor era data-rich era

Activities Data collection Data processingMajor effort Measurements Theory development and test Informatics Spreadsheet Eco-informaticsObjectives Discovery ForecastingMotives Curiosity-driven Decision makingService to society Long-term Real-time

Future prospects

TheoryReal-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