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

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

  • Data-Model Assimilation in Ecology

    History, present, and futureYiqi Luo

    University of Oklahoma

  • OutlineHistorical PerspectivePresent opportunitiesFuture prospects

  • Historical Perspective

  • Process thinkingData-model AssimilationSynthesis and predictionInformation contained in data

  • Approaches to scientific researchExperiment (observation)Model (Theory)Theory delineates possibilities

    Empirical studies discriminate the actualities

    Robert May 1981

  • Approaches to scientific researchExperimentModel TheoryDataProcesses thinking

  • Simple ecological models (1800s-1950s)1. Growth modelsLogistic growth equation Pierre Verhulst 18382. 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 researchExperimentModel TheoryDataProcesses thinkingSimple model

  • 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 literatureAnalysis, interpretation, and presentation of masses of numerical data.

  • Approaches to scientific researchExperimentModel TheoryDataProcesses thinkingSimple modelStatistic analysis Systems analysisProbability

  • Systems analysisFirst described by Heraclitus in 6th century BCActive research tools in 1930s-40sUsed in ecology in 1950s60s by Odum, Watt, and many others.Holistic analysis on structure and behavior of a system as a whole.

  • Approaches to scientific researchExperimentModel TheoryDataProcesses thinkingSimple modelStatistic analysis Systems analysisSimulation modelProbability

  • Simulation model(1960s- present)Forrester, J.W. 1961 Industry DynamicsDe Wit in Netherlands, 1960s 90sApplications in ecology 1960s presExample: CENTURYUsesSynthesis and integration of data Predicting the behavior of ecosystemsHypothesis generation for study designPolicy making.

  • Simulation model (cont.)Challenges

    Low confidence on model outputModel validation and testing against dataTransparency and amenability to analysis.

  • Approaches to scientific researchExperimentModel TheoryDataProcessesthinkingSimple modelStatistic analysis Systems analysisSimulation modelData-model assimilationProbability Baysian analysis

  • Techniques of Optimization in Data-model AssimilationStochastic inversion

    Bayesian inversion Thomas Beyes (1701 1761) Markov Chain Monte Carlo Metropolis-Hastings (1950s) Simulated annealing (Kirkpatrick et al. 1983) Genetic algorithms (Goldberg 1989)Deterministic inversion

    Steepest descendingNewton method Isaac Newton (1711)Newton-Gauss methodLevenburg-Marquardt algorithm (1944, 1963)

  • 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 ForecastingPotential 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) NetworkLTER 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 eraCharacteristicsData-poor eradata-rich era

    Activities Data collectionData processingMajor effortMeasurements Theory development and test InformaticsSpreadsheetEco-informaticsObjectivesDiscoveryForecastingMotivesCuriosity-drivenDecision makingService to societyLong-term Real-time

  • Future prospects

  • TheoryReal-time data stringsecological modelsData-model fusionEco-informaticsEcological forecasting NEON and other sensor networksDecision makingResource managementPreparation for catastrophe

  • Future researchEco-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

  • SummeryData and model are two foundational approaches to scientific inquiry about natural world. Data-model assimilation combines the bests from both approachesAs 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

    Forward model: where we predict the model state variables based on forcing data and parameters. We estimate the model parameters from ecophysiological knowledge extracted from literature and sometimes from experiments in the region of interest. A model can give the right answer but not necessarily because the right reasons (after you have done so much twicking of your parameters in order to come up with a final output which seems reasonable.Inverse model: rearrangement of the equations of the forward model so that we are estimating parameters from forcing data and observations of model state variables