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PEcAn: The Predictive Ecosystem Analyzer for Data-Driven Environmental Insights

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PEcAn (Predictive Ecosystem Analyzer) synthesizes heterogeneous data to bridge the gap between conceptual and computational models in ecology. It summarizes existing knowledge from available data and mechanistic models, identifies uncertainty sources, and prioritizes data collection and model enhancement. With a modular design, PEcAn offers high-level functions and a web interface for easy remote execution of simulations on High-Performance Computing (HPC). It supports various analytical modules, including meta-analysis, data assimilation, and visualization, facilitating data-driven decision-making in ecosystem management.

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PEcAn: The Predictive Ecosystem Analyzer for Data-Driven Environmental Insights

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  1. PEcAn The Predictive Ecosystem Analyzer

  2. Motivation • Synthesize heterogeneous data • Bridge gap between conceptual and computational models • Summarize what we know, based on available data and mechanistic models • Identify sources of uncertainty -> prioritize data collection and model improvement • Make complex workflows accessible, reproducible, and extensible

  3. Design • Modular: • models can be coupled within PEcAn • PEcAn can be embedded into other workflows • High level functions • e.g. ‘run.meta.analysis’; ‘start.model.runs(model)’ • Web Interface • Remote execution of simulation models on HPC • Adoption of existing standards, libraries where possible • Virtual Machines easy to get up and running

  4. Modules • Analysis: • Meta-analysis • Data assimilation • Visualization • Priors • Uncertainty • more … • Utilities: • QAQC • Database • Logger • Settings • Models (min 2 functions each): • Ecosystem Demography v2 • BioCro • Sipnet • Dalec

  5. Site BETYdb: Informatics Backend • Citation Treatment Management Cultivar Traits, Yields, Ecosystem Services Species Covariates Variable Prior Functional Type

  6. Site BETYdb (part II): Model provenance • Citation Treatment Management Runs Ensembles Cultivar Workflows Posteriors Site Species Models Inputs Traits, Yields, Ecosystem Services Machines Variable Covariates Functional Type Variable Prior Functional Type

  7. PEcAn: Web Interface Configure Run Review Previous Runs Visualize, Export Results Analysis in R

  8. Future Directions • Model Intercomparisons • Integration into existing workflows • Automated ‘real-time’ data assimilation • Improved web-interface – enable end users to ask new questions

  9. More Information • Who: • David LeBauer, University of Illinois • Mike Dietze, Boston University • Rob Kooper, National Center for Supercomputing Applications • Shawn Serbin, Brookhaven National Laboratories • Where: • pecanproject.org • github.com/PecanProject • Funding: • Energy Biosciences Institute, NSF

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