Model-data fusion for vegetation models and also some other things

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Presentation transcript:

Model-data fusion for vegetation models and also some other things Florian Hartig, University of Regensburg

Personal Background Born close to Frankfurt, Germany Studied physics in Berlin and Uppsala (Sweden) PhD in Leipzig on agent-based simulations of land use decisions Postdoc in Leipzig on biodiversity patterns and community assembly in tropical rainforests Lecturer (Biostatistics) at Uni Freiburg Currently professor (Theoretical Ecology) at Uni Regensburg

Research Areas/Interests Community ecology / biogeography / macroecology / theoretical ecology Statistics and machine learning Classical biostatistics: inference, model selection, … Some machine learning applications Simulation-based inference Approximate inference for stochastic simulations in ecology and evolution (Synthetic Likelihood, Approximate Bayesian Computation, … ) Calibration / data-assimilation / model-data fusion Model-data connection, mostly for dynamic vegetation models

Bayesian model-data fusion for vegetation models Hartig et al., J. Biogeogr., 2012

BayesianTools R package https://cran. r-project Algorithms for model-data fusion Sampling Algorithms Various Metropolis algorithms Differential Evolution DREAM Sequential Monte Carlo Support for Bayesian tasks (plots, statistics) Optimization Sensitivity Analysis Plotting Parallelization

Links to „Computer Science meets Ecology“ Machine learning approaches Filling sparse trait matrices „trait-matching“ / “syndromes”: predicting ecological interactions or characteristics based on trait data Model-data fusion Efficient MCMC and SMC algorithms Model-data integration for dynamic vegetation models Simulation General interest in HPC methods Simulating large (10^9) numbers of individuals in parallel