INDITEK-2.0: A Bayesian inverse eco-evolutionary modelling framework for reconstructing Phanerozoic biodiversity

Mechanistic eco-evolutionary models are powerful tools for understanding how life diversified, but estimating their parameters is a major challenge. The vast parameter space of these models makes traditional inverse modelling computationally unfeasible. We address this challenge with INDITEK-2.0, a new, cost-efficient eco-evolutionary model that integrates a Bayesian inverse modelling framework for parameter estimation. To validate our framework, we conducted a proof-of-concept study. We first generated synthetic biodiversity data for marine invertebrates using INDITEK-2.0 with known parameter values. To mimic field data, some realistic random Gaussian errors were added to the data. We then used our Bayesian inverse modelling framework to recover these original parameter values from the dataset of current biodiversity distributions after 500 million years of diversification. Our solution is a probability distribution on the parameter space, and we show that the method successfully recovered the target parameters values. This validates INDITEK-2.0's ability to probabilistically infer the underlying eco-evolutionary processes and reconstruct the evolutionary history of animal biodiversity across Earth's ecosystems and taxonomic groups.