lotvolt: Discrete-time Lotka-Volterra competition model from Loreau et...

In seananderson/ecofoliosim: Simulations of community dynamics for portfolio optimization

Description

Simulate from an expanded discrete-time Lotka-Volterra competition model as described in Loreau et al. 2013.

Usage

lotvolt(rm, k, b, n0, sigma_d, sigma_e, u_d, u_e)

Arguments

rm	A numeric vector of intrinsic maximum rate of natural increase for each species.
k	A numeric vector of carrying capacities for each species.
b	A numeric matrix of competition coefficients between species i (columns) and species j (rows).
n0	A numeric vector of starting biomass for each species.
sigma_d	A vector of demographic standard deviation magnitudes for each species.
sigma_e	A vector of environmental standard deviation magnitudes for each species.
u_d	A matrix of standard normal N(0, 1) demographic deviations. Rows are time steps and columns are species.
u_e	A matrix of standard normal N(0, 1) environmental deviations. Rows are time steps and columns are species.

Details

Note that synchrony in environmental dynamics and demographic processes can be added through the matrices u_d and u_e. Temporal autocorrelation can be added through these matrices as well.

References

Loreau, M. and de Mazancourt, C. (2008). Species synchrony and its drivers: Neutral and nonneutral community dynamics in fluctuating environments. Amer. Nat., 172(2):E48-E66.

Loreau, M. (2010). From Populations to Ecosystems: Theoretical Foundations for a New Ecological Synthesis. Princeton University Press, Princeton, NJ.

Loreau, M. and de Mazancourt, C. (2013). Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol. Lett., 16(S1):106-115.

See Also

lotvolt2

Examples

# Figure 1b in Loreau et al. 2013:
species <- 2
time_steps <- 550
burnin <- 50
u_d <- matrix(ncol = species, nrow = time_steps,
  data = rnorm(species * time_steps, 0, 1))
u_e <- matrix(ncol = species, nrow = time_steps,
  data = rnorm(species * time_steps, 0, 1))
m <- lotvolt(rm = c(0.5, 0.8), k = c(1000, 1500),
  b = rbind(c(NA, 0.7), c(0.9, NA)),
  n0 = c(1000, 1500), sigma_d = c(1, 1), sigma_e = c(0.02, 0.02),
  u_d = u_d, u_e = u_e)
matplot(m[-seq_len(burnin), ], type = "l", ylab = "Biomass",
  lty = 1, ylim = c(0, 1000), xlab = "Time")

seananderson/ecofoliosim documentation built on May 29, 2019, 4:25 p.m.