COS 251-3 - Equilibrium theory of biodiversity dynamics: Identifying patterns, parameters, and predictions

Abstract: Recent evidence supports the existence of biodiversity limits at multiple spatiotemporal scales, yet the mechanisms behind these limits remain poorly understood. The Equilibrium Theory of Biodiversity dynamics (ETBD) suggests that there is a carrying capacity for species richness (an equilibrium) which is driven by the relationship between energy availability, species richness, total abundance, and population size dependent extinction and/or origination rates. Many large-scale ecological theories such as this are limited in their development due to poorly defined or untestable predictions. To avoid this pitfall, we aim to fully explore the parameters and relationships within this theory to identify novel predictions and gain further understanding of the mechanisms behind biodiversity limits in this context.
To achieve this, we developed an R package to produce simulation models that generate patterns in species richness and evolution via the assumptions of ETBD. We performed a full factorial exploration of parameter space and used AMOVA models to evaluate the relative importance of initial conditions and parameters on the variation (dissimilarity) of emergent patterns.
We show that ETBD can function under ecologically realistic parameters. Additionally, we found that simulations run using population size dependent origination rates produce more realistic evolutionary patterns than simulations run using population size dependent extinction rates. Furthermore, we predict that communities acting under ETBD and following a log-series species abundance distribution (SAD) will maintain higher diversification rates and lower equilibria compared to communities following a log-normal SAD. We identified parameter combinations that result in stable equilibria and determined extinction probabilities for parameter combinations that result in unstable or stochastic equilibria. Our results provide support and testable predictions for an emerging ecological theory and deepen our understanding of the possible mechanisms responsible for large scale patterns in biodiversity and evolution.