Environment that changes over time

Adds to the environment of each site a deviation that follows an independent first-order autoregressive process with lag-one autocorrelation rho and stationary standard deviation sd, starting from its stationary distribution. This is a simple generator of temporal autocorrelation, not the one used by Thompson et al. (2020).

Usage

env_over_time(env, n_steps, rho = 0.9, sd = 5)

Arguments

env

Environment of each site, e.g. the env of an mc_landscape.

n_steps

Number of time steps.

rho

Lag-one autocorrelation of the deviations, in [0, 1).

sd

Stationary standard deviation of the deviations.

Value

Matrix with one row per site and one column per time step, for the env_time argument of simulate_metacommunity().

Examples

set.seed(1)
e <- env_over_time(c(20, 50, 80), n_steps = 100, rho = 0.95, sd = 5)
matplot(t(e), type = "l", lty = 1, xlab = "step", ylab = "environment")