gen3sis: the general engine for eco-evolutionary simulations on the origins of biodiversity

Abstract

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially-explicit, eco-evolutionary engine with a modular implementation that enables the modelling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatio-temporally dynamic landscapes. Modelled processes can include environmental filtering, biotic interactions, dispersal, speciation and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β and γ diversity, species ranges, ecological traits and phylogenies, emerge as simulations proceed. As a case study, we examined alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth's Cenozoic era. We found that a carrying capacity linked with energy was the only model variant that could simultaneously produce a realistic LDG, species range size frequencies, and phylogenetic tree balance. The model engine is open source and available as an R-package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a step towards a numeric and mechanistic understanding of the physical and biological processes that shape Earth's biodiversity.

Deep Learning from Phylogenies for Diversification Analyses

“…It could be used for example to represent information on species multidimensional traits, geographic distributions, abundances, and genetic diversity. Combined with efficient simulation models for the evolution of biodiversity (Hagen et al 2021), the CNN-CDV deep learning inference approach could help adjusting biologically realistic biodiversity models to multifaceted data for a better understanding of how present-day biodiversity was generated, maintained, and distributed geographically.…”

“…g ., insects, micro-eukaryotes and prokaryotes) difficult. As a result, there are several models of diversification in the literature which behavior has been studied with simulations but that lack a proper inference machinery (McPeek 2008; Aristide and Morlon 2019; Hagen et al 2021). Methods based on Expectation Maximization (EM) algorithms (Dempster et al 1977; Richter et al 2020), data augmentation (Maliet and Morlon 2022), or composite likelihoods (Lindsay 1988; Varin et al 2021)) can overcome some of these limitations (Raynal 2019), yet they still rely on likelihood formulae.…”

South American freshwater fish diversity shaped by Andean uplift since the Late Cretaceous

“…Using the mechanistic biodiversity model gen3sis 16 , we simulate biodiversity dynamics in a geographical framework through three mechanisms: dispersal, speciation, and extinction. The dispersal rate (distance per time step) determines how far species populations disperse through suitable habitat cells.…”

“…Gen3sis (GENeral Engine for Eco-Evolutionary SImulationS 16 ) is a mechanistic model simulating the evolution of biodiversity as a function of habitat change, through the mechanisms of dispersal, speciation, and extinction. Habitat change is characterized by environmental conditions related to landscape evolution in a geographical framework, and is in this study represented by changes in suitable (rivers, lakes) or non-suitable (land, marine) habitat.…”

“…Instead, it requires an approach in which the ambiguous effects of river reorganizations can be assessed in a process-based way, and in which paleogeographic changes are not considered to be 'instantaneous' (in the context of geological time, within a million year), but continuous. In this study, we use a mechanistic modelling approach 16 , simulating dispersal, allopatric speciation and extinction over a dynamic landscape of rivers and lakes. We investigate the role of Andean mountain building and Fig.…”