Welcome to the icom digital twin project

Anthropogenic environmental change is fundamentally altering natural systems, and assessing how different environmental stressors affect multiple interacting species is critical to advance both theoretical ecology and applied management. Under this premise, ecological forecasting has emerged as a research frontier, but to improve our predictions of the effects of global-change drivers across ecological scales, we need “upscaling” models that integrate processes from individual interactions to ecosystem functioning. These models can identify key biological pathways through which systems can be buffered from or become more vulnerable to stressors. However, such upscaling is challenging because the necessary dynamic models that replicate the complexity of natural systems, i.e., digital twins, must couple high-output biological computing and detailed ecological knowledge on key components of a target system. The present project (ICOM) proposes to utilize decades of ecological data collected in Doñana Natural Area, coupled with advanced methods in computational biology, to develop a scalable and integrated model, a digital twin, for Doñana. 

ICOM has three main objectives: 

(i) To create a dynamic database structure to obtain abiotic and biotic information from Doñana Natural Area by digitizing and continuously updating, via dynamic workflows, primary data 

(ii) To develop new methods in data integration based on high-output latent state computing which allow to join disparate data types and scales into one modelling framework 

(iii) To develop a digital twin that scales up, from individuals to ecosystems, the effects of interacting global-change on terrestrial ecosystems in Doñana Natural Area 

This unprecedented coordination of digital technology, empirical data integration, and complex-system modelling will be achieved through a collaboration between EBD and the Computational Biology and Complex Systems Group at UPC . Digital twins are completely lacking in Mediterranean ecosystems, and this collaboration has the potential to position the CSIC as a leader in predictive ecology, pioneering a new generation of mechanistic forecasting research to answer the fundamental question: Does ongoing climate change interacting with other global-change drivers lead to loss of biodiversity and ecosystem functions? Answering this question is of key relevance for current EU policy that aims to strengthen predictions of human impacts on biodiversity.