ecolottery: Simulating and assessing community assembly with environmental filtering and neutral dynamics inR

Abstract

We introduce the R package ecolottery dedicated to quick and efficient simulation of communities undergoing local neutral dynamics with environmentally filtered immigration from a reference species pool (spatially implicit model). The package includes an Approximate Bayesian Computation (ABC) tool to estimate the parameters of these processes. We present the rationale of the approach and show examples of simulations and ABC analysis. The species in the reference pool differ in their abundances and trait values. Environmental filtering weights the probability of immigration success depending on trait values, while the descendants of established immigrants undergo neutral stochastic drift. The reference pool can be defined in a flexible way as representing, e.g. the composition of a broad biogeographical region, or available dispersers around local communities. The package provides a process‐based alternative to the use of randomization‐based null models. The package proposes a coalescent‐based simulation algorithm that presents significant advantages over alternative algorithms. It does not require simulating community dynamics from an initial state forward in time but does still allow measurement of the influence of environmental filtering. Because of its high calculation speed, this approach allows simulating many communities within a reasonable amount of time. Diverse patterns of taxonomic, functional and phylogenetic compositions can be generated. The package can be used to explore the outcome of ecological and evolutionary processes playing at local and regional scales, and to estimate the parameters of these processes based on observed patterns.

Tropical forest type influences community assembly processes in arbuscular mycorrhizal fungi

“…This indicates that there are major differences in abiotic variables between sites. By contrast, species associations were mainly found across species at the same sites, in accordance with the assumption that biotic interactions occur at a lower spatial scale (Munoz et al, ). This demonstrates the importance of spatial scales for generating null hypotheses, and future studies should therefore consider this when designing sampling strategies.…”

Identifying models of trait‐mediated community assembly using random forests and approximate Bayesian computation

“…While we feel CAMI will continue to make progress in advancing our understand of community ecological patterns globally, there are still many aspects of community ecological theory yet to be incorporated (Belyea & Lancaster, ; Weiher et al, ). The assembly models defined here could be made more powerful by considering other community dynamics such speciation, colonization, and extinction during the assembly process (Rosindell & Harmon, ), as well as co‐occurring and structured non‐neutral processes (Keddy & Shipley, ) where the relative importance of these processes can be measured (as in Munoz et al, ; van der Plas et al, ). These aspects may be more or less relevant depending on the taxonomic scale of the community being investigated (Weiher et al, ).…”

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal, environmental filtering, and stochasticity on community assembly

“…We adopted the approach of Sokol, Brown, and Barrett () to build a metacommunity simulation program in R (Supporting information Appendix ) that allowed us to simulate metacommunities that consisted of several local communities assembled with or without spatially restricted dispersal, with varying degrees of habitat connectivity (i.e., immigration rates) and with strong, intermediate, and no environmental filtering. Other spatially implicit simulation approaches allow simulating metacommunity dynamics under environmental filtering, stochastic dynamics, and immigration rates (Munoz et al, ). However, a strength of the simulation approach of Sokol et al () is that it is spatially explicit so that the pool of potential immigrants that can reach a community changes as the metacommunity evolves, that is, the simulated metacommunities never reach a stable equilibrium.…”

“…We implemented the spatial correlation by applying a Gaussian filter with sigma values 0.2 for environmental conditions, and 0.4 for community sizes to a raster map with uniformly distributed values. This ensured that environmental gradients were steeper than community size gradients (Figure 1a We adopted the approach of Sokol, Brown, and Barrett (2017) to build a metacommunity simulation program in R (Supporting (Munoz et al, 2018). However, a strength of the simulation approach of Sokol et al (2017) is that it is spatially explicit so that the pool of potential immigrants that can reach a community changes as the metacommunity evolves, that is, the simulated metacommunities never reach a stable equilibrium.…”