simulate_pheno_ts: Compute phenotypic dynamic Time Series of trait + demographic...
In ashander/phenoecosim: Simulations of eco-evo trait and demographic dynamics under changing environments

Description Usage Arguments Details Value

Compute phenotypic dynamic Time Series of trait + demographic change under stabilizing selection as function of environment (after Lande Chevin)

1 2	simulate_pheno_ts(T, X, params, env_args, growth_fun = "independent", poisson = FALSE, varying_g = FALSE)

`T`	end time, assuming start time of 1
`X`	parameters (z, a, b, wbar, logN, theta)
`params`	a list with (gamma_sh, omegaz, A, B, R0, var_a, Vb, delta, sigma_xi, rho_tau, fractgen)
`env_args`	extra args for env.fn
`growth_fun`	density-dependence ("independent", "gompertz", "thetalogistic", "ceiling")
`poisson`	(logical) return N(t+1) = Poisson(f(N(t)). NOTE: only implemented for independent and thetalog!
`varying_g`	varying genetic variance using stochastic house of cards (SHC) approximation. See Details.

NB - for now assume Tchange = 0 and demography after CL 2010 For the SHC after Burger and Lynch (1995) and Kopp and Matuszewski (2013) \[ \sigma_SHC() = \frac2 V_m V_s1 + \fracV_s\alpha ^ 2 N_e \] For $\alpha^2$ the variance of the effect of new mutations, $V_m$ the genomic mutation rate per generation, and $N_e \approx 2 R_0 N / (2 R_0 - 1)$. As population size increases, the function approaches a limit of $2 V_s V_m$ $V_s = \omega^2 + \sigma_e^2$ (we hard code the latter as 1) is the width of the fitness function.

a long matrix with columns zbar, abar, bbar, Wbar, Npop, theta

ashander/phenoecosim documentation built on May 10, 2017, 10:16 p.m.