MetaIBM: A Python']Python-based library for individual-based modelling of eco-evolutionary dynamics in spatial-explicit metacommunities

Individual-based modelling (IBM) is a powerful tool for simulating complex biological communities. By defining a population as comprising individuals that differ from one another, IBM can simulate the assembly and organisation of complex communities under various eco-evolutionary processes in a large spatial scale, with tremendous variables or parameters considered simultaneously. IBM disentangles a complex system into various sub-systems interacting with each other, allowing us to develop a unified library with a modular design for a wide range of complex scenarios in community assembly. In such a library, a number of parameters-controlled processes can be primitively coded as the sub-systems (or sub-models). Here, we released a Python-coded library as a framework for Metacommunity Individual-based Modelling (MetaIBM). As an open-source library, the MetaIBM has several merits, including: (a) it can be used to simulate a wide range of ecological problems of metacommunities. The metacommunity landscape and its environment gradients can be designed flexibly by users. Users can selectively turn off or on and set up parameters-controlled ecological processes according to their needs. (b) It adopts optimised algorithms and adapts to the high-performance computing devices, so that the users can explore a wide range of parameters space synchronously within a reasonable time. (c) It can be used to simulate a group of communities with up to millions of unique individuals, which is an originally plain portrayal of natural communities. To guide potential users, we provided the source codes of the library and a user manual. In the present article, we gave four examples to demonstrate how to design and model a metacommunity using the MetaIBM, simulating the community assembly in an islands-mainland model under the metacommunity framework with (a) neutral assumptions, (b) niche assumptions, (c) slow evolution scenarios, (d) rapid evolution scenarios. The examples showed that the MetaIBM can efficiently fit the community assembly, and reveal several intrigued species diversity patterns under the interaction of evolutionary processes and dispersal processes at multiple scales. The MetaIBM will be continuously maintained and updated to provide more functions in the future.