stoch.sens {demogR} | R Documentation |
Calculates the sensitivity of the stochastic growth rate to perturbations in the mean demographic projection matrix.
stoch.sens(env, amat, k)
env |
environmental sequence |
amat |
matrices describing the population dynamics in the different environmental states, organized as columns of a matrix |
k |
rank of the projection matrices |
See Caswell (2001, section 14.4) for details.
A list with two elements:
sensitivities |
sensitivities of the stochastic growth rate |
elasticities |
elasticities of the stochastic growth rate |
This is essentially a translation of Caswell's (2001, section 14.4.1.1) MATLAB code.
Caswell, H. 2001. Matrix population models: Construction, analysis, and interpretation. 2nd ed. Sunderland, MA: Sinauer.
Haridas, C. V., and S. Tuljapurkar. 2005. Elasticities in variable environments: Properties and implications. American Naturalist 166 (4):481-495.
Tuljapurkar, S. 1990. Population dynamics in variable environments. Vol. 85, Lecture notes in biomathematics. Berlin: Springer-Veralg.
Tuljapurkar, S., and C. V. Haridas. 2006. Temporal autocorrelation and stochastic population growth. Ecology Letters 9 (3):324-334.
## Simulate an i.i.d. sequence of 3 environmental states env <- floor(runif(100,0,3))+1 px1 <- rbeta(4,9.5,0.5) px2 <- rbeta(4,7.5,2.5) px3 <- rbeta(4,5,5) mx <- c(0,rgamma(4,5,10)) A1 <- odiag(px1,-1) A2 <- odiag(px2,-1) A3 <- odiag(px3,-1) A1[1,] <- leslie.row1(mx,px1) A2[1,] <- leslie.row1(mx,px2) A3[1,] <- leslie.row1(mx,px3) amat <- cbind(matrix(A1,nr=25), matrix(A2,nr=25), matrix(A3,nr=25)) stoch.sens(env,amat,k=5)