Analysing ecological dynamics with relational event models: The case of biological invasions
Rūta Juozaitienė, Hanno Seebens, Guillaume Latombe, Franz Essl, Ernst C. Wit
Abstract
Abstract Aims Spatio‐temporal processes play a key role in ecology, from genes to large‐scale macroecological and biogeographical processes. Existing methods studying such spatio‐temporally structured data either simplify the dynamic structure or the complex interactions of ecological drivers. The aim of this paper is to present a generic method for ecological research that allows analysing spatio‐temporal patterns of biological processes at large spatial scales by including the time‐varying variables that drive these dynamics. Location Global analysis at the level of 272 regions. Methods We introduce a method called relational event modelling (REM). REM relies on temporal interaction dynamics that encode sequences of relational events connecting a sender node to a recipient node at a specific point in time. We apply REM to the spread of alien species around the globe between 1880 and 2005, following accidental or deliberate introductions into geographical regions outside of their native range. In this context, a relational event represents the new occurrence of an alien species given its former distribution. Results The application of relational event models to the first reported invasions of 4835 established alien species outside of their native ranges from four major taxonomic groups enables us to unravel the main drivers of the dynamics of the spread of invasive alien species. Combining the alien species first records data with other spatio‐temporal information enables us to discover which factors have been responsible for the spread of species across the globe. Besides the usual drivers of species invasions, such as trade, land use and climatic conditions, we also find evidence for species‐interconnectedness in alien species spread. Conclusions Relational event models offer the capacity to account for the temporal sequences of ecological events such as biological invasions and to investigate how relationships between these events and potential drivers change over time.