DescriptionIsland biogeography models play an important role in understanding evolutionary patterns and processes of species on isolated systems. While many simulation models have been developed in island biogeography, a major challenge is to estimate evolutionary and biogeographical parameters such as colonization, speciation and extinction from phylogenetic data. A maximum likelihood approach has been used to estimate parameters of the island biogeography model DAISIE (Dynamic Assembly of Island biota through Speciation, Immigration and Extinction). However, with the increasing model complexity required for more mechanistic hypotheses in island biogeography, it is often not possible to develop likelihood functions to gain tractable solutions and information in the data may be limited for estimating large numbers of parameters. Approximate Bayesian Computation (ABC) has been proposed as a likelihood-free method to overcome these difficulties. Here, we develop an ABC framework for the island biogeography model DAISIE, with the aim of estimating parameters from complex evolutionary scenarios. We test the inference ability of the ABC approach and investigate what the most informative summary statistics are. Furthermore, we apply the ABC framework to an extension of the DAISIE model with trait state-dependent colonization and diversification rates, to study whether trait dynamics affect the diversity of island species.
|Event title||Mathematical Models in Ecology and Evolution 2022|
|Location||Reading, United Kingdom|
|Degree of Recognition||International|