CAUSATION: Assessing causal relationships in Macroevolution

Throughout earth’s history, a myriad of factors has driven biodiversity patterns.

During the lifespan of lineages, biotic and abiotic variables might act differently in shaping the diversification dynamics of organisms.

Therefore identifying and quantifying their relative importance is crucial for a better understanding of past and current biodiversity patterns.

This is particularly relevant for complex systems involving multiple functional groups spread through different trophic levels, e.g. the system composed of browsing and grazing animals and the plants they consume.

The dynamics of the whole system can be affected not only by the biological components but also by environmental factors.

Recently, new methods have been proposed to directly address causal associations between time series, based on dynamic systems theory, which represents an advancement in relation to traditional correlation-based approaches.

This proposal will assess the causal associations between the diversity of each component of the plant-herbivore system to have a better understanding of the mechanisms involved in the regulation of biodiversity in macroevolutionary timescales.

Using the “Empirical Dynamic Modelling” (EDM) approach, which is a non-parametric approach specifically designed to address causality in time series, I will test the performance of this approach when using simulated and empirical macroevolutionary time series, applying it to the diversity time series of the components of the plant-herbivore system.

This project will produce a critical examination of the performance of the EDM approach in this context, as well as produce direct information on the causal associations between the diversity of different groups and the influence of environmental variables on the dynamics of the whole system.