heating_pulse2: Heating pulse-2

Description Usage Arguments Details Value References Examples

View source: R/heating_pulse2.R

Description

This function allows simulating the effect of an environmental warming pulse on the abundance of ectotherm populations. After the pulse, the temperature stabilizes at a temperature q units greater than the initial value (temp_ini).

Usage

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heating_pulse2(
  y_ini = c(N = 400, N = 400, N = 400),
  temp_ini = rep(20, 3),
  temp_cmin = rep(18, 3),
  temp_cmax = c(25, 28, 32),
  ro = rep(0.7, 3),
  lambda = rep(5e-05, 3),
  temp_peak = rep(25, 3),
  time_peak = rep(2060, 3),
  q = rep(5, 3),
  time_start = 2005,
  time_end = 2100,
  leap = 1/12
)

Arguments

y_ini

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

temp_ini

Initial temperature.

temp_cmin

Minimum critical temperature.

temp_cmax

Maximum critical temperature.

ro

Population growth rate at optimum temperature.

lambda

Marginal loss by non-thermodependent intraspecific competition.

temp_peak

Peak pulse temperature.

time_peak

Time when temp_peak is reached.

q

Difference between initial and stabilization temperature.

time_start

Start of time sequence.

time_end

End of time sequence.

leap

Time sequence step.

Details

Three populations and/or scenarios can be simulated simultaneously. The temperature trend is determined by a rational function in which the temperature stabilizes at a different value after the pulse (that is, the final temperature differs from the initial temperature by q units). In each input vector, the parameters for the three simulations must be specified (finite numbers for the initial population abundance). The simulations are obtained by a model that incorporates the effects of temperature over time, which leads to a non-autonomous ODE approach. This is function uses the ODE solver implemented in the package deSolve (Soetaert et al., 2010).

Value

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

(2) A two-panel figure in which (a) shows the population abundance curves represented by solid lines and the corresponding carrying capacities are represented by shaded areas. In (b) the temperature trend is shown. The three simultaneous simulations are depicted by different colors, i.e. 1st brown, 2nd green and 3rd blue.

References

Soetaert, K., Petzoldt, T., & Setzer, R. (2010). Solving Differential Equations in R: Package deSolve. Journal of Statistical Software, 33(9), 1 - 25. doi:http://dx.doi.org/10.18637/jss.v033.i09

Examples

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#######################################################################
  #Example 1: Different initial population abundances.
#######################################################################

heating_pulse2(y_ini = c(N = 100, N = 200, N = 400),
               temp_ini = rep(20,3),
               temp_cmin = rep(18,3),
               temp_cmax = rep(30,3),
               ro = rep(0.7,3),
               lambda = rep(0.00005,3),
               temp_peak = rep(29,3),
               time_peak = rep(2060,3),
               q = rep(3,3),
               time_start = 2005,
               time_end = 2100,
               leap = 1/12)

#######################################################################
  #Example 2: Different thermal tolerance ranges.
#######################################################################

temp_cmin3 <- 18
temp_cmin2 <- 10/9*temp_cmin3
temp_cmin1 <- 10/9*temp_cmin2

temp_cmax1 <- 32.4
temp_cmax2 <- 10/9*temp_cmax1
temp_cmax3 <- 10/9*temp_cmax2


heating_pulse2(y_ini = c(N=100,N=100,N=100),
              temp_ini = rep(23,3),
              temp_cmin = c(temp_cmin1,temp_cmin2,temp_cmin3),
              temp_cmax = c(temp_cmax1,temp_cmax2,temp_cmax3),
              ro = rep(0.7,3),
              lambda = rep(0.00005,3),
              temp_peak = rep(35,3),
              time_peak = rep(2060,3),
              q = rep(1,3),
              time_start = 2005,
              time_end = 2100,
              leap = 1/12)

#######################################################################
  #Example 3: Different relationships between initial environmental
  #           temperature and optimum temperature.
#######################################################################

temp_cmin <- 18
temp_cmax <- 30

# Temperature at which performance is at its maximum value.
temp_op <- (temp_cmax+temp_cmin)/3+sqrt(((temp_cmax+temp_cmin)/3)^2-
           (temp_cmax*temp_cmin)/3)

temp_ini1 <- (temp_cmin+temp_op)/2
temp_ini2 <- temp_op
temp_ini3 <- (temp_op+temp_cmax)/2

heating_pulse2(y_ini = c(N = 100, N = 100, N = 100),
              temp_ini = c(temp_ini1,temp_ini2,temp_ini3),
              temp_cmin = rep(temp_cmin,3),
              temp_cmax = rep(temp_cmax,3),
              ro = rep(0.7,3),
              lambda = rep(0.00005,3),
              temp_peak = rep(29,3),
              time_peak = rep(2060,3),
              q = rep(1,3),
              time_start = 2005,
              time_end = 2100,
              leap = 1/12)

#######################################################################
  #Example 4: Different peaks of temperature.
#######################################################################

temp_peak3 <- 30
temp_peak2 <- 9/10*temp_peak3
temp_peak1 <- 9/10*temp_peak2

heating_pulse2(y_ini = c(N = 100, N = 100, N = 100),
              temp_ini = rep(19,3),
              temp_cmin = rep(18,3),
              temp_cmax = rep(30,3),
              ro = rep(0.7,3),
              lambda = rep(0.00005,3),
              temp_peak = c(temp_peak1,temp_peak2,temp_peak3),
              time_peak = rep(2060,3),
              q = rep(1,3),
              time_start = 2005,
              time_end = 2100,
              leap = 1/12)

#######################################################################
  #Example 5: Different marginal losses by a non-thermodependent
  #           component of intraspecific competition.
#######################################################################

lambda3 <- 0.01
lambda2 <- 1/2*lambda3
lambda1 <- 1/2*lambda2

heating_pulse2(y_ini = c(N = 100, N = 100, N = 100),
              temp_ini = rep(26,3),
              temp_cmin = rep(18,3),
              temp_cmax = rep(30,3) ,
              ro = rep(0.7,3),
              lambda = c(lambda1,lambda2,lambda3),
              temp_peak = rep(29,3),
              time_peak = rep(2075,3),
              q = rep(1,3),
              time_start = 2005,
              time_end = 2100,
              leap = 1/12)

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