epcc (version 1.4.7)

age_structure: Age structure under IPCC RCP2.6 or RCP8.5 scenarios

Description

This function allows to simulate the effect of IPCC (2014) RCP2.6 or RCP8.5 scenarios on the abundances of an ectotherm population considering three stages as age structure.

Usage

age_structure(
  y_ini = c(N1 = 800, N1 = 800, N1 = 800, N2 = 600, N2 = 600, N2 = 600, N3 = 400, N3 =
    400, N3 = 400),
  temp_ini = rep(25 + 273.15, 3),
  temp_cmin = rep(18 + 273.15, 3),
  temp_cmax = c(25 + 273.15, 28 + 273.15, 35 + 273.15),
  ro = rep(0.7, 3),
  lambda1 = rep(4e-04, 3),
  lambda2 = rep(4e-04, 3),
  lambda3 = rep(4e-04, 3),
  alpha1 = rep(0.1, 3),
  alpha2 = rep(0.7, 3),
  d2 = rep(0.005, 3),
  d3 = rep(0.5, 3),
  Ad2 = rep(0.5, 3),
  Ad3 = rep(0.75, 3),
  Tr2 = rep(298, 3),
  Tr3 = rep(298, 3),
  RCP = 2.6,
  time_start = 2005,
  time_end = 2100,
  leap = 1/50
)

Arguments

y_ini

Initial population values (must be written with its name: N).

temp_ini

Initial temperature (K).

temp_cmin

Minimum critical temperature (K).

temp_cmax

Maximum critical temperature (K).

ro

Population growth rate at optimum temperature.

lambda1, lambda2, lambda3

Marginal loss by non-thermodependent intraspecific competition.

alpha1, alpha2

Stage 1 to 2 and 2 to 3 transition coefficient respectively.

d2, d3

Mortality rate at a reference temperature.

Ad2, Ad3

Arrhenius constant which quantifies the temperature sensitivity of mortality.

Tr2, Tr3

Reference temperature (K).

RCP

Representative concentration trajectories (RCP2.6 and RCP8.5 scenarios).

time_start

Start of time sequence.

time_end

End of time sequence.

leap

Time sequence step.

Value

(1) A data.frame with columns having the simulated trends.

(2) Figure of four panels in which (a), (b) and (c) show the population abundance curves for each age stage. Panel (d) shows the temperature trend curves used for each simulation, colored brown, green and blue, respectively.

Details

Three scenarios can be evaluated for a predation interaction where the prey is an ectotherm population. The temperature trends correspond to IPCC projections under the RCP2.6 or RCP8.5 scenarios. In each input vector, the parameters for the three simulations must be specified.

References

IPCC. (2014): Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Examples

# NOT RUN {
#######################################################################
  #Example 1: Different thermal tolerance ranges (scenario RCP2.6).
#######################################################################

temp_cmin <- 291

# Temperature that occurs before the minimum simulation time.
temp_i <- 295

time_end <- 2100

# Temperature that occurs in the maximum time of the simulation.
temp_max <- get_RCP2.6(time_end)+temp_i

# Simulation thermal range.
RS <- temp_max-temp_cmin

temp_cmax1 <- 4/3*RS+temp_cmin
temp_cmax2 <- 2/3*RS+temp_cmin
temp_cmax3 <- 1/3*RS+temp_cmin
temp_ini <- (temp_cmin+temp_cmax3)/2

age_structure(y_ini = c(N1 = 800, N1 = 800, N1 = 800,
                       N2 = 600, N2 = 600, N2 = 600,
                       N3 = 400, N3 = 400, N3 = 400),
             temp_ini = rep(temp_ini,3),
             temp_cmin = rep(temp_cmin,3),
             temp_cmax = c(temp_cmax1,temp_cmax2,temp_cmax3),
             ro = rep(0.7,3),
             lambda1 = c(0.00002,0,0),
             lambda2 = c(0,0.00004,0.00003),
             lambda3 = c(0,0.00003,0.00004),
             alpha1 = rep(0.3,3),
             alpha2 = rep(0.4,3),
             d2 = rep(0.004,3),
             d3 = rep(0.005,3),
             Ad2 = rep(0.5,3),
             Ad3 = rep(0.75,3),
             Tr2 = rep(298,3),
             Tr3 = rep(298,3),
             RCP = 2.6,
             time_start = 2005,
             time_end = time_end,
             leap = 1/50)

#######################################################################
  #Example 2: Different thermal tolerance ranges (scenario RCP8.5).
#######################################################################

temp_cmin <- 291

# Temperature that occurs before the minimum simulation time.
temp_i <- 295

time_end <- 2100

# Temperature that occurs in the maximum time of the simulation.
temp_max <- get_RCP8.5(time_end)+temp_i

# Simulation thermal range.
RS <- temp_max-temp_cmin

temp_cmax1 <- 4/3*RS+temp_cmin
temp_cmax2 <- 2/3*RS+temp_cmin
temp_cmax3 <- 1/3*RS+temp_cmin
temp_ini <- (temp_cmin+temp_cmax3)/2

age_structure(y_ini = c(N1 = 800, N1 = 800, N1 = 800,
                       N2 = 600, N2 = 600, N2 = 600,
                       N3 = 400, N3 = 400, N3 = 400),
             temp_ini = rep(temp_ini,3),
             temp_cmin = rep(temp_cmin,3),
             temp_cmax = c(temp_cmax1,temp_cmax2,temp_cmax3),
             ro = rep(0.7,3),
             lambda1 = c(0.00002,0,0),
             lambda2 = c(0,0.00004,0.00003),
             lambda3 = c(0,0.00003,0.00004),
             alpha1 = rep(0.3,3),
             alpha2 = rep(0.4,3),
             d2 = rep(0.004,3),
             d3 = rep(0.003,3),
             Ad2 = rep(0.5,3),
             Ad3 = rep(0.6,3),
             Tr2 = rep(298,3),
             Tr3 = rep(298,3),
             RCP = 8.5,
             time_start = 2005,
             time_end = time_end,
             leap = 1/50)
# }