gen3sis
gen3sis simulates spatially-explicit eco-evolutionary dynamics to investigate the origins and large-scale patterns of biodiversity.
Key Features:
- Spatially-explicit modelling: Represents dynamic landscapes over time to capture spatio-temporal ecological and evolutionary processes.
- Modular implementation: Provides a modular architecture that enables customization and integration of macroecological and macroevolutionary processes.
- Comprehensive process modelling: Simulates environmental filtering, biotic interactions, dispersal, speciation, and the evolution of ecological traits.
- Emergence of biodiversity patterns: Produces emergent α (alpha), β (beta), and γ (gamma) diversity, species range dynamics, ecological trait distributions, and phylogenies.
- Case study application: Applied to the Cenozoic latitudinal diversity gradient (LDG), showing that a model variant with energy-linked carrying capacity reproduced realistic LDG, species range size frequencies, and phylogenetic tree balance.
Scientific Applications:
- Macroecology and macroevolution: Investigates large-scale ecological and evolutionary mechanisms that generate biodiversity patterns.
- Hypothesis testing: Tests alternative scenarios and model variants (for example LDG mechanisms) by comparing simulated outcomes.
- Empirical data integration: Enables comparison of simulated diversity, range, trait, and phylogenetic patterns with empirical biodiversity data.
Methodology:
Implements a numeric, mechanistic, spatially-explicit simulation framework with a modular architecture to simulate environmental filtering, biotic interactions, dispersal, speciation, and trait evolution, and is implemented as an R package.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- library
- Programming Languages:
- R, C++
- Added:
- 9/20/2021
- Last Updated:
- 9/20/2021
Operations
Publications
Hagen O, Flück B, Fopp F, Cabral JS, Hartig F, Pontarp M, Rangel TF, Pellissier L. gen3sis: the general engine for eco-evolutionary simulations on the origins of biodiversity. Unknown Journal. 2021. doi:10.1101/2021.03.24.436109.