predation: Predation under IPCC RCP2.6 or RCP8.5 scenarios

In epcc: Simulating Populations of Ectotherms under Global Warming

Description

This function allows simulating the effect of the IPCC RCP2.6 or RCP8.5 scenarios (2014) on the abundances of two species interacting through predation. The prey is an ectotherm population, and the predator is not affected by temperature.

Usage

predation(
  y_ini = c(V = 400, V = 400, V = 400, P = 200, P = 200, P = 200),
  temp_ini = rep(25, 3),
  temp_cmin = rep(18, 3),
  temp_cmax = c(25, 28, 32),
  ro = rep(0.7, 3),
  lambda = rep(5e-05, 3),
  e = rep(0.3, 3),
  mp = rep(0.08, 3),
  q = rep(0.7, 3),
  a = rep(800, 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.
temp_cmin	Minimum critical temperature (prey).
temp_cmax	Maximum critical temperature (prey).
ro	Population growth rate at optimum temperature (prey).
lambda	Marginal loss by non-thermodependent intraspecific competition (prey).
e	Efficiency with which food is converted into population growth (predator).
mp	Mortality rate (predator).
q	Maximum per capita consumption rate (predator).
a	Mean saturation rate (predator).
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.

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.

Value

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

(2) A four-panel figure where (a), (b), and (c) show the population abundance curves for each simulation. The brown curve corresponds to the abundance of prey and the green curve to predators. Panel (d) shows the temperature trend curves used for each simulation, green, blue, and black, respectively.

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

#######################################################################
  #Example 1: Different thermal tolerance ranges (scenario RCP2.6).
#######################################################################

temp_cmin <- 18

# Temperature that occurs before the minimum simulation time.
temp_i <- 22

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

predation(y_ini = c(V = 800, V = 800, V = 800,
                   P = 600, P = 600, P = 600),
         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),
         lambda = rep(0.0004,3),
         e = rep(0.9,3),
         mp = rep(0.1,3),
         q = rep(0.7,3),
         a = rep(1000,3),
         RCP = 2.6,
         time_start = 2005,
         time_end = time_end,
         leap = 1/50)

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

temp_cmin <- 18

# Temperature that occurs before the minimum simulation time.
temp_i <- 22

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

predation(y_ini = c(V = 800, V = 800, V = 800,
                   P = 600, P = 600, P = 600),
         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),
         lambda = rep(0.0004,3),
         e = rep(0.9,3),
         mp = rep(0.1,3),
         q = rep(0.7,3),
         a = rep(1000,3),
         RCP = 8.5,
         time_start = 2005,
         time_end = time_end,
         leap = 1/50)

#######################################################################
  #Example 3: Different conversion efficiencies (scenario RCP2.6).
#######################################################################

e1 <- 0.2
e2 <- 2*e1
e3 <- 2*e2

predation(y_ini = c(V = 800, V = 800, V = 800,
                   P = 400, P = 400, P = 400),
         temp_ini = rep(22,3),
         temp_cmin = rep(20,3),
         temp_cmax = rep(35,3),
         ro = rep(0.9,3),
         lambda = rep(0.0006,3),
         e = c(e1,e2,e3),
         mp = rep(0.1,3),
         q = rep(0.7,3),
         a = rep(800,3),
         RCP = 2.6,
         time_start = 2005,
         time_end = 2100,
         leap = 1/50)

#######################################################################
  #Example 4: Different conversion efficiencies (scenario RCP8.5).
#######################################################################

e1 <- 0.2
e2 <- 2*e1
e3 <- 2*e2

predation(y_ini = c(V = 800, V = 800, V = 800,
                   P = 400, P = 400, P = 400),
         temp_ini = rep(22,3),
         temp_cmin = rep(20,3),
         temp_cmax = rep(35,3),
         ro = rep(0.9,3),
         lambda = rep(0.0006,3),
         e = c(e1,e2,e3),
         mp = rep(0.1,3),
         q = rep(0.7,3),
         a = rep(800,3),
         RCP = 8.5,
         time_start = 2005,
         time_end = 2100,
         leap = 1/50)

epcc documentation built on June 29, 2021, 9:07 a.m.