ecolottery-package: Coalescent-Based Simulation of Ecological Communities

In ecolottery: Coalescent-Based Simulation of Ecological Communities

Coalescent-Based Simulation of Ecological Communities

Description

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

Details

The DESCRIPTION file: This package was not yet installed at build time.
Index: This package was not yet installed at build time.
Two basic functions: coalesc for coalescent-based simulation, and forward for forward-in-time simulation

Author(s)

François Munoz [aut, cre], Matthias Grenié [aut], Pierre Denelle [aut], Adrien Taudière [ctb], Fabien Laroche [ctb], Caroline Tucker [ctb], Cyrille Violle [ctb]

Maintainer: François Munoz <[e-mail]>

References

Hurtt, G. C. and S. W. Pacala (1995). "The consequences of recruitment limitation: reconciling chance, history and competitive differences between plants." Journal of Theoretical Biology 176(1): 1-12.

Hubbell, S. P. (2001). "The Unified Neutral Theory of Biodiversity". Princeton University Press.

Gravel, D., C. D. Canham, M. Beaudet and C. Messier (2006). "Reconciling niche and neutrality: the continuum hypothesis." Ecology Letters 9(4): 399-409.

Munoz, F., P. Couteron, B. R. Ramesh and R. S. Etienne (2007). "Estimating parameters of neutral communities: from one Single Large to Several Small samples." Ecology 88(10): 2482-2488.

Munoz, F., B. R. Ramesh and P. Couteron (2014). "How do habitat filtering and niche conservatism affect community composition at different taxonomic resolutions?" Ecology 95(8): 2179-2191.

Examples

## Coalescent-based simulation of stabilizing habitat filtering around 
## t = 0.5
J <- 100; theta <- 50; m <- 0.5;
comm <- coalesc(J, m, theta, filt = function(x) 0.5 - abs(0.5 - x))
plot_comm(comm)

## Forward-in-time simulation of stabilizing habitat filtering around 
## t = 0.5, over 100 time steps

# A regional pool including 100 species each including 10 individuals
pool <- sort(rep(as.character(1:100), 10))

# Initial community composed of 10 species each including 10 individuals, 
# with trait information for niche-based dynamics
initial <- data.frame(sp = sort(rep(as.character(1:10), 10)),
                      trait = runif(100))
final <- forward(initial = initial, prob = 0.5, gens = 100, pool = pool,
                 filt = function(x) 0.5 - abs(0.5 - x))
plot_comm(final)

ecolottery documentation built on April 15, 2025, 1:35 a.m.