A guest post by Oskar Hagen (iDiv) | www.hagen.bio | @hagen_oskar
What is the cause and what are the processes that gave rise to Earth’s biodiversity patterns through space and time? Much research has been devoted to describing these patterns, and over the years, the fields of macroecology and macroevolution have slowly transitioned from a mainly correlational to a more mechanistic perspective (1, 2). The challenge with understanding the mechanisms of macroevolution is that, while evolution has in principle simple general rules, it operates across a complex dynamic world. As a result, there is only so much we can understand with simple theoretical and empirical models – for a more detailed understanding of the diversification of life on earth, we will require models that reflects not only ecological and evolutionary processes, but also the complexity in spatio-temporal drivers of the system, in particular changes in climatic and geographic patterns over evolutionary time scales. Such flexible eco-evolutionary models that use realistic dynamic landscapes allow to realistically compare candidate processes leading to the emergence of biodiversity patterns (such as past and present α, β, and γ diversity, species ranges, ecological traits, and phylogenies) against empirical evidence.
In this post, I share the story of the development of gen3sis (1), an exciting new simulation engine that hopefully will bring us closer to uncover some of the mysteries behind Earth’s biodiversity. gen3sis stands in the tradition of scientists moving from simple mathematical to more complex computational models (3), and my academic development followed a somewhat similar path. Around 2013 I developed a generalized phylogenetic tree simulator (TreeSimGM) based on multiple probability density functions for speciation and extinction (Bellman Harris Model) together with T. Stadler (4). We found that age-dependent speciation best explained empirical topologies (tree shape balance) (5). However, linking such abstract probability functions to real processes is difficult and limited to hypothesis formulation and further speculation. In 2016 I dug deeper into the biological mechanisms underlying biodiversity dynamics by adding more detailed ecological processes to an existing spatially explicit macroevolutionary model (SPLIT) written by P. Descombes, T. Gaboriau, F. Leprieur, L. Pellissier and others (6, 7). This allowed us to investigate the emergence of global biodiversity patterns.
Informed by my previous experiences on generalising a birth-death model inside a new context, I wanted to build a more modular and flexible simulation engine which became gen3sis: general engine for eco-evolutionary simulations (1). The idea was to overcome the limitations of simple models that do not consider explicit spatial-temporal changes or spatial models that are built around fixed assumptions and ignore or limit experimentation of ecological, evolutionary as well as complex interactions. By allowing for custom ecological and evolutionary process and interactions in an explicit dynamic landscape, we can better predict and understand diversification under changing conditions and expose complex processes at multiple temporal and spatial scales.
During this time, gen3sis’ architecture changed multiple times and its development involved multiple interdisciplinary contributions, including the dialog between software engineers, geologists, modelers, and empiricists. For example, Benjamin Flück, a software engineer, joined the team and helped optimizing code (e.g. R to C++) and passing selected functions into a customizable configuration file. This relaxation of eco-evolutionary rules input over a configuration file demanded further thoughts on functions and parameters naming, for proper mechanisms categorization and intuitive model use. Important to this naming and process definition process was the involvement of the Landscape Ecology and an sDiv synthesis group with participants from multiple backgrounds and specific ecological or evolutionary perspectives. Finding a balance between speed, generality and usability was a long trial and error process.
The result is a modelling engine that for the first time offers the ability to simulate almost any scenario for extraordinary insights to life on earth from deep-time to large spatial scales. Gen3sis keep track of differentiation between populations, allowing for distance decrease after secondary contact, while permitting multiple traits that can evolve and interact with biotic and abiotic components linking ecological and evolutionary processes. Non changeable and central to the model are the calculations of clusters of connected populations, which are based on universal principles of geneflow between populations in a spatial context and dependent on dispersal abilities. Initial conditions as well as other modelled processes including speciation, dispersal, trait evolution and ecology are changeable and interconnected in a very customizable and intuitive way.
For example, take speciation, which is essential to understanding the emergence of biodiversity. In most phylogenetic macro-evolutionary simulators, speciation happens according to a probability density function in a space-less fashion. In gen3sis, new species results from a set of rules (functions informed by a user defined configuration file and speciation happens in allopatry, after populations are spatially isolated for a certain period of time. This isolation can depend on: (1) species dispersal abilities which can evolve and tradeoff with other traits; (2) landscape connectivity which can consider barriers (e.g. land for aquatic or water for terrestrial organism) and change over time (e.g. a reconstructed paleolandscape); (3) ecological processes which can modulate abundances or presences considering abiotic and biotic conditions as well as (4) evolutionary processes that dictate persistence under changing conditions or adaptation to new settings. Additional mechanisms and feedbacks are possible, such as the inclusion of temperature effects on mutation, or metabolic rates. Consequently, model complexity is customizable, allowing us to test and see if we can differentiate between models.
Gen3sis is more than just developing an eco-evolutionary model to answer one specific question. Gen3sis is a general engine allows the formalization and testing of ecological and evolutionary processes happening in complex and dynamic landscapes. Gen3sis’ flexibility opens up a wide range of future applications, demonstrated in a case study accompanying the methods publication on PLOS Biology addressing the latitudinal diversity gradient (1). On another – soon to be published – study, gen3sis revealed the importance of palaeoenvironmental dynamics, rather than current climatic factors, on the formation of uneven distribution of biodiversity across tropical regions. Currently, I am using gen3sis to study local processes and better scale mechanism in space, time and levels of complexity using regional metacommunity eco-evolutionary experiments.
Exciting other possible future applications could address causal links between biodiversity and: (a) orogenetic and/or erosion models; (b) aquatic ecological and/or evolutionary processes; (c) temperature and/or water availability; (d) climatic variations; (e) intraspecific genetic variability; (f) functional traits such as niche width and dispersal abilities as well as (g) emerging interaction networks. Practical use could involve long term conservation planning, such as wildlife corridors, or modeling the spreading of infectious diseases under multiple scenarios (e.g. COVID). Alternatively and personally very interesting for me is that gen3sis can contribute to fields that are traditionally not relying on biological principles, such as cultural and technological evolution. For more nonexhaustive expected applications of gen3sis see Table 4 in (1).
While we are far from predicting the emergence of biodiversity patterns on Earth, gen3sis offers an open source tool able to simulate gradual changes influenced by multiple factors in constant interaction over a long period of time. This has the potential to advance knowledge in multiple, interdisciplinary research areas. Gen3sis is available as an R-package on CRAN along beginners’ tutorials, in order to facilitate use, dialog and support of other scientists to piece together key puzzles of the Earth’s astonishing biodiversity. Available on github under GPL3, gen3sis inspires to provide open model development inside a critical and varied community. For this, you are more than welcome to join!
I thank Florian Hartig, Laura Méndez and Emma Ladouceur for comments and feedbacks.
- short historical perspective see monography introduction (~25min read)
- another blog post commenting on gen3sis (~15min read)
- R-package github and CRAN repository
1. O. Hagen, B. Flück, F. Fopp, J. S. Cabral, F. Hartig, M. Pontarp, T. F. Rangel, L. Pellissier, gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity. PLOS Biol. 19, e3001340 (2021).
2. M. Pontarp, L. Bunnefeld, J. S. Cabral, R. S. Etienne, S. A. Fritz, R. Gillespie, C. H. Graham, O. Hagen, F. Hartig, S. Huang, R. Jansson, O. Maliet, T. Munkemuller, L. Pellissier, T. F. Rangel, D. Storch, T. Wiegand, A. H. Hurlbert, The latitudinal diversity gradient: Novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).
3. M. Weisberg, Simulation and Similarity: Using Models to Understand the World (OUP USA, 2013; https://books.google.de/books?id=rDu5e532mIoC), Oxford Studies in Philosophy of Science.
4. O. Hagen, T. Stadler, TreeSimGM: Simulating phylogenetic trees under general Bellman-Harris models with lineage-specific shifts of speciation and extinction in R. Methods Ecol Evol. 9, 754–760 (2018).
5. O. Hagen, K. Hartmann, M. Steel, T. Stadler, Age-dependent speciation can explain the shape of empirical phylogenies. Syst. Biol. 64, 432–440 (2015).
6. F. Leprieur, P. Descombes, T. Gaboriau, P. F. Cowman, V. Parravicini, M. Kulbicki, C. J. Melian, C. N. de Santana, C. Heine, D. Mouillot, D. R. Bellwood, L. Pellissier, Plate tectonics drive tropical reef biodiversity dynamics. Nat. Commun. 7, 11461 (2016).
7. P. Descombes, F. Leprieur, C. Albouy, C. Heine, L. Pellissier, Spatial imprints of plate tectonics on extant richness of terrestrial vertebrates. J. Biogeogr. 44, 1185–1197 (2017).