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How to integrate individual-based long-term monitoring and satellite-based landscape dynamics for biodiversity predictions
Billur Bektaş1, Patrícia Singh2, Maria Paniw3, Mary Lofton4, Freya Olsson4
1Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland; 2Department of Botany and Zoology, Faculty of Science, Masaryk University, Czech Republic; 3Doñana Biological Station (EBD-CSIC), Sevilla, Spain; 4Center for Ecosystem Forecasting, Virginia Tech, Blacksburg, USA
Description: If you work with individual monitoring data of plants and animals, you have probably wondered: How do I predict changes in community or population dynamics beyond my study site? This hands-on demonstration bridges the gap between local monitoring and landscape-scale predictions. Through worked examples in R, you will learn to integrate individual plant monitoring data with satellite remote sensing to forecast biodiversity dynamics across multiple scales over space and time.
We will guide you through the process of combining fine-scale biological monitoring with satellite data to predict population and landscape changes under various environmental scenarios. You will gain practical experience in joining different data types and building streamlined prediction workflows. You will explore how these dynamic workflows have the potential to improve over time, offering increasingly accurate predictions as more data becomes available. As a highlight, you will participate in Europe's one of the first biodiversity forecasting challenges, where you can submit and evaluate forecasts using our real-world use case.
Using R, Git, and Github, you will gain experience in designing automated workflows that continuously update biodiversity models with new data. We will conclude by exploring future directions for satellite-biodiversity data integration and identify priorities for improving spatiotemporal predictions through better models, data collection, and integration frameworks. Prior knowledge in R, Github account and your personal computer are required if you would like to participate in the forecasting challenge.
