R/increasing_linear.R

In epcc: Simulating Populations of Ectotherms under Global Warming

#'Projection of increasing linear temperature
#'
#' @description This function allows simulating the effect of a linear increase in environmental
#'              temperature on the abundance of ectotherm populations.
#'
#'
#'@param y_ini Initial population values (must be written with its name: N).
#'@param temp_ini Initial temperature.
#'@param temp_cmin Minimum critical temperature.
#'@param temp_cmax Maximum critical temperature.
#'@param ro Population growth rate at optimum temperature.
#'@param m Temperature trend growth slope.
#'@param lambda Marginal loss by non-thermodependent intraspecific competition.
#'@param time_start Start of time sequence.
#'@param time_end End of time sequence.
#'@param leap Time sequence step.
#'
#'@details Three populations and/or scenarios can be simulated simultaneously.
#'         The temperature trend is determined by a linear function. The slope
#'         can be specified. 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).
#'
#'
#'@return (1) A data.frame with columns having the simulated trends.
#'@return (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
#'
#'@export
#'@examples
#'
#'#######################################################################
#'   #Example 1: Different initial population abundances.
#'#######################################################################
#'
#'increasing_linear(y_ini = c(N = 100, N = 200, N = 400),
#'                  temp_ini = rep(26,3),
#'                  temp_cmin = rep(18,3),
#'                  temp_cmax = rep(40,3),
#'                  ro = rep(0.7,3),
#'                  m = rep(0.15,3),
#'                  lambda = rep(0.00005,3),
#'                  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
#'
#'increasing_linear(y_ini = c(N = 100, N = 100, N = 100),
#'                  temp_ini = rep(26,3),
#'                  temp_cmin = c(temp_cmin1,temp_cmin2,temp_cmin3),
#'                  temp_cmax = c(temp_cmax1,temp_cmax2,temp_cmax3),
#'                  ro = rep(0.7,3),
#'                  m = rep(0.15,3),
#'                  lambda = rep(0.00005,3),
#'                  time_start = 2005,
#'                  time_end = 2100,
#'                  leap = 1/12)
#'\donttest{
#'#######################################################################
#'   #Example 3: Different relationships between initial environmental
#'   #           temperature and optimum temperature.
#'#######################################################################
#'
#'temp_cmin <- 18
#'temp_cmax <- 40
#'
#'# 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
#'
#'increasing_linear(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),
#'                  m = rep(0.15,3),
#'                  lambda = rep(0.00005,3),
#'                  time_start = 2005,
#'                  time_end = 2100,
#'                  leap = 1/12)
#'
#'#######################################################################
#'   #Example 4: Different marginal losses by a non-thermodependent
#'   #           component of intraspecific competition.
#'#######################################################################
#'
#'lambda3 <- 0.01
#'lambda2 <- 1/2*lambda3
#'lambda1 <- 1/2*lambda2
#'
#'increasing_linear(y_ini = c(N = 100, N = 100, N = 100),
#'                  temp_ini = rep(26,3),
#'                  temp_cmin = rep(18,3),
#'                  temp_cmax = rep(36,3),
#'                  ro = rep(0.7,3),
#'                  m = rep(0.15,3),
#'                  lambda = c(lambda1,lambda2,lambda3),
#'                  time_start = 2005,
#'                  time_end = 2100,
#'                  leap = 1/12)
#'}


###################################################

increasing_linear<- function(y_ini = c(N=100,N=200,N=400),
                             temp_ini = rep(26,3),
                             temp_cmin = rep(18,3),
                             temp_cmax = rep(40,3),
                             ro = rep(0.7,3),
                             m = rep(0.15,3),
                             lambda = rep(0.00005,3),
                             time_start = 2005,
                             time_end = 2100,
                             leap = 1/12){

  times<- seq(time_start, time_end, leap)

if(temp_cmin[1]<temp_cmax[1] && temp_cmin[2]<temp_cmax[2] && temp_cmin[3]<temp_cmax[3] ){

if(temp_cmin[1]<=temp_ini[1] && temp_ini[1]<=temp_cmax[1] && temp_cmin[2]<=temp_ini[2] &&
   temp_ini[2]<=temp_cmax[2] && temp_cmin[3]<=temp_ini[3] && temp_ini[3]<=temp_cmax[3]){



      LC<- function (times,temp_ini,m,time_start) {
        T <- temp_ini+m*(times-time_start)

      }


##########################################################
# Optimum growing temperature
##########################################################

temp_op1<-(temp_cmax[1]+temp_cmin[1])/3+sqrt(((temp_cmax[1]+
            temp_cmin[1])/3)^2-(temp_cmax[1]*temp_cmin[1])/3)
temp_op2<-(temp_cmax[2]+temp_cmin[2])/3+sqrt(((temp_cmax[2]+
            temp_cmin[2])/3)^2-(temp_cmax[2]*temp_cmin[2])/3)
temp_op3<-(temp_cmax[3]+temp_cmin[3])/3+sqrt(((temp_cmax[3]+
            temp_cmin[3])/3)^2-(temp_cmax[3]*temp_cmin[3])/3)

##########################################################
# Time
##########################################################

time_op1=suppressWarnings((temp_op1-temp_ini[1])/m[1]+time_start)
time_cmin1=suppressWarnings((temp_cmin[1]-temp_ini[1])/m[1]+time_start)
time_cmax1=suppressWarnings((temp_cmax[1]-temp_ini[1])/m[1]+time_start)

time_op2=suppressWarnings((temp_op2-temp_ini[2])/m[2]+time_start)
time_cmin2=suppressWarnings((temp_cmin[2]-temp_ini[2])/m[2]+time_start)
time_cmax2=suppressWarnings((temp_cmax[2]-temp_ini[2])/m[2]+time_start)

time_op3=suppressWarnings((temp_op3-temp_ini[3])/m[3]+time_start)
time_cmin3=suppressWarnings((temp_cmin[3]-temp_ini[3])/m[3]+time_start)
time_cmax3=suppressWarnings((temp_cmax[3]-temp_ini[3])/m[3]+time_start)


if(times[length(times)]<time_cmax1){
  tm_new1=times[length(times)]
}else{tm_new1=time_cmax1}

if(times[length(times)]<time_cmax2){
  tm_new2=times[length(times)]
}else{tm_new2=time_cmax2}

if(times[length(times)]<time_cmax3){
  tm_new3=times[length(times)]
}else{tm_new3=time_cmax3}

##########################################################
# Parameters
##########################################################
parms1<-c(temp_cmin[1],temp_ini[1],temp_cmax[1],temp_op1,ro[1], lambda[1])
parms2<-c(temp_cmin[2],temp_ini[2],temp_cmax[2],temp_op2,ro[2], lambda[2])
parms3<-c(temp_cmin[3],temp_ini[3],temp_cmax[3],temp_op3,ro[3], lambda[3])

##########################################################
    # Model for each trend
##########################################################
model1 <- function (times, y,parms1) {
      with(as.list(c(y)), {
  T <- LC(times,temp_ini[1],m[1],time_start)
  r1<- rate_TPC(T,ro[1],temp_cmin[1],temp_cmax[1],temp_op1)
  dN <-   r1 * N * (1 - lambda[1]*(N / r1))
  list(dN,T,r1) })
    }
################################################################
model2 <- function (times, y,parms2) {
      with(as.list(c(y)), {
  T <- LC(times,temp_ini[2],m[2],time_start)
  r2<- rate_TPC(T,ro[2],temp_cmin[2],temp_cmax[2],temp_op2)
  dN <-   r2 * N * (1 - lambda[2]*(N / r2))
  list(dN,T,r2)})
    }
###############################################################
model3 <- function (times, y,parms3) {
      with(as.list(c(y)), {
  T <- LC(times,temp_ini[3],m[3],time_start)
  r3<- rate_TPC(T,ro[3],temp_cmin[3],temp_cmax[3],temp_op3)
  dN <-   r3 * N * (1 - lambda[3]*(N / r3))
  list(dN,T,r3)})
    }
###############################################################
    # Solution
##############################################################
out1 <- ode(y=y_ini[1], times, model1, parms1,method = "ode45")
out2 <- ode(y=y_ini[2], times, model2, parms2,method = "ode45")
out3 <- ode(y=y_ini[3], times, model3, parms3,method = "ode45")
#############################################################

###############################################################
# Temperature trend
##############################################################

    da1<-data.frame('x'=times,'y'=out1[,3] )
    da2<-data.frame('x'=times,'y'=out2[,3] )
    da3<-data.frame('x'=times,'y'=out3[,3] )

###############################################################
    # Abundance
##############################################################
    data1<-data.frame('x'=times,'y'=out1[,2] )
    data2<-data.frame('x'=times,'y'=out2[,2] )
    data3<-data.frame('x'=times,'y'=out3[,2] )
###############################################################
# Carrying capacity
##############################################################
    K1=out1[,4]/lambda[1]
    K2=out2[,4]/lambda[2]
    K3=out3[,4]/lambda[3]

    dat1<-data.frame('x'=times,'y'=K1 )
    dat2<-data.frame('x'=times,'y'=K2 )
    dat3<-data.frame('x'=times,'y'=K3 )

###############################################################
# Data
###############################################################
Data<- data.frame(times,out1[,3],out1[,2],K1,out2[,3],out2[,2],
                  K2,out3[,3],out3[,2],K3)
names(Data)<- c("Time","Temperature Scenario 1","Abundance scenario 1",
                "Carrying capacity scenario 1","Temperature scenario 2",
                "Abundance scenario 2","Carrying capacity scenario 2",
                "Temperature scenario 3","Abundance scenario 3","Carrying
                capacity scenario 3")
u<- formattable(Data, align = c("l", rep("r", NCOL(Data))))
print(u)

###############################################################
    # Plots
##############################################################

data<-rbind(data1,data2,data3,dat1,dat2,dat3,da1,da2,da3)

p1 <- ggplot(data, aes(x=.data$x, y=.data$y)) +
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
geom_ribbon(data=subset(dat1,times>times[1]  & times<tm_new1),aes(x=.data$x,
                                ymax=.data$y),ymin=0,alpha=0.3,fill="brown") +
geom_ribbon(data=subset(dat2,times>times[1]  & times<tm_new2),aes(x=.data$x,
                               ymax=.data$y),ymin=0,alpha=0.3,fill="green4") +
geom_ribbon(data=subset(dat3,times>times[1]  & times<tm_new3),aes(x=.data$x,
                                ymax=.data$y),ymin=0,alpha=0.3, fill="blue") +
geom_vline(xintercept = tm_new1, size=.5, color="brown",linetype="dashed")+
geom_vline(xintercept = tm_new2, size=.5, color="green4",linetype="dashed")+
geom_vline(xintercept = tm_new3, size=.5, color="blue",linetype="dashed")+
geom_line(data =subset(data1,times>times[1]  & times<tm_new1), color = "brown")+
geom_line(data =subset(data2,times>times[1]  & times<tm_new2), color = "green4")+
geom_line(data =subset(data3,times>times[1]  & times<tm_new3), color = "blue")+
labs(x = "Time",y="Abundance")+
theme(legend.text=element_text(size=7), legend.title=element_text(size=rel(0.5)))+
theme(axis.title.y = element_text(size = rel(1), angle = 90))+
theme(axis.title.x = element_text(size = rel(1), angle = 00))+
labs(tag = "(a)")


p2 <- ggplot(data, aes(x=.data$x, y=.data$y)) +
theme_bw()+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
geom_vline(xintercept = tm_new1, size=.5, color="brown",linetype="dashed")+
geom_vline(xintercept = tm_new2, size=.5, color="green4",linetype="dashed")+
geom_vline(xintercept = tm_new3, size=.5, color="blue",linetype="dashed")+
geom_line(data =subset(da1,times>times[1]  & times<tm_new1), color = "brown")+
geom_line(data =subset(da2,times>times[1]  & times<tm_new2), color = "green4")+
geom_line(data =subset(da3,times>times[1]  & times<tm_new3), color = "blue")+
labs(x = "Time",y="Temperature")+
theme(axis.title.y = element_text(size = rel(1), angle = 90))+
theme(axis.title.x = element_text(size = rel(1), angle = 00))+
labs(tag = "(b)")


 plot_grid(p1, p2)


  }else{
stop("The initial study temperature must be within the thermal tolerance range")
    }

}else{

stop("The minimum critical temperature must be less than the maximum critical temperature")
  }

}