Massive Eco-evolutionary Synthesis Simulations (MESS)

MESS is a novel comparative phylogeographic model grounded in community ecological theory. This integrative approach makes use of four data axes (distributions of traits, abundances, genetic diversities/divergences, and phylogenetic patterns) to enable testing alternative community assembly models (neutral vs non-neutral) and estimating parameters underlying different assembly processes (e.g. dispersal vs in situ speciation). This method capitalizes on the widespread use of DNA barcoding and meta-barcoding approaches.

What kind of data it requires

MESS requires sequence data from population-level sampling (5-10 individuals per species) from one or multiple ecological communities of organims. This can be at a variety of scales ranging from a microbial community within a host individual, a locally sampled plot targeting everything from a taxonomic group, to a regional assemblage that emerged via disersal and/or local speciation. Currently only single locus data is supported, so community metabarcoding projects would be quite appropriate. Other data types can be included but are not required (abundances, per taxon trait metrics, and phylogenetic information).


MESS is implemented in python and the code is available on github.

Try it now!

Experiment with the MESS model now! Launch the binder instance below and you can open and run the notebooks in the jupyter-notebooks directory.


The MESS manuscript is available on bioarxiv:

Overcast I, Ruffley M, Rosindell J, Harmon L, Borges PA, Emerson BC, ... &
    Rominger A. (2020). A unified model of species abundance, genetic
    diversity, and functional diversity reveals the mechanisms structuring
    ecological communities. BioRxiv.

MESS is based on previous work on the gimmeSAD joint neutral model and the CAMI trait-mediated community assembly model which can be found here:

Overcast I, Emerson BC, Hickerson MJ. (2019). An integrated model of
    population genetics and community ecology. Journal of Biogeography,
    46: 816-829.

Ruffley M, Peterson K, Week B, Tank DC, & Harmon LJ. (2019). Identifying
    models of trait‐mediated community assembly using random forests and
    approximate Bayesian computation. Ecology and Evolution, 9(23), 13218-