competition: Interspecific competition under the influence of temperature...
In Victor-Saldana/epcc: Simulating Populations of Ectotherms under Global Warming
competition R Documentation
Interspecific competition under the influence of temperature trend adapted from the IPCC
projection (RCP2.6 or RCP8.5 scenarios)
Description
This function allows simulating the effect of temperature trends according
to RCP2.6 or RCP8.5 scenarios (2014) on the abundances of two competing species, where one of
them is ectothermic.
Usage
competition(
y_ini = c(N1 = 400, N1 = 400, N1 = 400, N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25, 3),
temp_cmin = rep(18, 3),
temp_cmax = c(25, 28, 35),
ro = rep(0.7, 3),
r2 = rep(0.7, 3),
lambda1 = rep(5e-05, 3),
K2 = rep(5e-05, 3),
alpha = rep(0.002, 3),
beta = rep(0.03, 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.
temp_cmax
Maximum critical temperature.
ro
Population growth rate at optimal temperature of species-1.
r2
Population growth rate of species-2.
lambda1
Marginal loss a by non-thermodependent intraspecific competition factor of species-1.
K2
Carrying capacity of species-2.
alpha
Competition coefficient that quantifies the per capita effect of species-2 on species-1.
beta
Per capita competition coefficient that quantifies the per capita effect of species-1 on species-2.
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
The function allows simulating simultaneously three potential outcomes for the interaction of
two competing populations where one is an ectothermic species. The temperature trends that can
be specified corresponds to IPCC projections under the RCP2.6 or RCP8.5 scenarios.
Value
(1) A data.frame with columns having the simulated trends.
(2) A four-panel figure where (a), (b), and (c) show the abundance curves of the populations for each
simulation, where the brown curve corresponds to the abundance of the ectotherm species and
the green curve to the species not affected by temperature. Panel (d) shows the temperature
trend curves, as they may differ for each simulation, these will be displayed by the colors
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
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 300, N2 = 300, N2 = 300),
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),
r2 = rep(0.7,3),
lambda1 = rep(0.0005,3),
K2 = rep(1400,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
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
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 300, N2 = 300, N2 = 300),
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),
r2 = rep(0.7,3),
lambda1 = rep(0.0005,3),
K2 = rep(1400,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
RCP = 8.5,
time_start = 2005,
time_end = time_end,
leap = 1/50)
#######################################################################
#Example 3: Different marginal losses by a non-thermodependent
# component of intraspecific competition for species-1
# (scenario RCP2.6).
#######################################################################
lambda3 <- 0.002
lambda2 <- 1/2*lambda3
lambda1 <- 1/2*lambda2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = c(lambda1,lambda2,lambda3),
K2 = rep(1200,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
RCP = 2.6,
time_start = 2005,
time_end = 2100,
leap = 1/50)
#'#######################################################################
#Example 4: Different marginal losses by a non-thermodependent
# component of intraspecific competition for species-1
# (scenario RCP8.5).
#######################################################################
lambda3 <- 0.002
lambda2 <- 1/2*lambda3
lambda1 <- 1/2*lambda2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = c(lambda1,lambda2,lambda3),
K2 = rep(1200,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
RCP = 8.5,
time_start = 2005,
time_end = 2100,
leap = 1/50)
#######################################################################
#Example 5: Different competition coefficients (scenario RCP2.6).
#######################################################################
alpha1 <- 0.02
alpha2 <- 2*alpha1
alpha3 <- 2*alpha2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = rep(0.0005,3),
K2 = rep(1200,3),
alpha = c(alpha1,alpha2,alpha3),
beta = rep(0.3,3),
RCP = 2.6,
time_start = 2005,
time_end = 2100,
leap = 1/50)
#######################################################################
#Example 6: Different competition coefficients (scenario RCP8.5).
#######################################################################
alpha1 <- 0.02
alpha2 <- 2*alpha1
alpha3 <- 2*alpha2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = rep(0.0005,3),
K2 = rep(1200,3),
alpha = c(alpha1,alpha2,alpha3),
beta = rep(0.3,3),
RCP = 8.5,
time_start = 2005,
time_end = 2100,
leap = 1/50)
Victor-Saldana/epcc documentation built on Oct. 18, 2023, 6:57 a.m.
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| competition | R Documentation |
Interspecific competition under the influence of temperature trend adapted from the IPCC projection (RCP2.6 or RCP8.5 scenarios)
Description
This function allows simulating the effect of temperature trends according to RCP2.6 or RCP8.5 scenarios (2014) on the abundances of two competing species, where one of them is ectothermic.
Usage
competition(
y_ini = c(N1 = 400, N1 = 400, N1 = 400, N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25, 3),
temp_cmin = rep(18, 3),
temp_cmax = c(25, 28, 35),
ro = rep(0.7, 3),
r2 = rep(0.7, 3),
lambda1 = rep(5e-05, 3),
K2 = rep(5e-05, 3),
alpha = rep(0.002, 3),
beta = rep(0.03, 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. |
temp_cmax |
Maximum critical temperature. |
ro |
Population growth rate at optimal temperature of species-1. |
r2 |
Population growth rate of species-2. |
lambda1 |
Marginal loss a by non-thermodependent intraspecific competition factor of species-1. |
K2 |
Carrying capacity of species-2. |
alpha |
Competition coefficient that quantifies the per capita effect of species-2 on species-1. |
beta |
Per capita competition coefficient that quantifies the per capita effect of species-1 on species-2. |
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
The function allows simulating simultaneously three potential outcomes for the interaction of two competing populations where one is an ectothermic species. The temperature trends that can be specified corresponds to IPCC projections under the RCP2.6 or RCP8.5 scenarios.
Value
(1) A data.frame with columns having the simulated trends.
(2) A four-panel figure where (a), (b), and (c) show the abundance curves of the populations for each simulation, where the brown curve corresponds to the abundance of the ectotherm species and the green curve to the species not affected by temperature. Panel (d) shows the temperature trend curves, as they may differ for each simulation, these will be displayed by the colors 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
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 300, N2 = 300, N2 = 300),
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),
r2 = rep(0.7,3),
lambda1 = rep(0.0005,3),
K2 = rep(1400,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
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
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 300, N2 = 300, N2 = 300),
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),
r2 = rep(0.7,3),
lambda1 = rep(0.0005,3),
K2 = rep(1400,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
RCP = 8.5,
time_start = 2005,
time_end = time_end,
leap = 1/50)
#######################################################################
#Example 3: Different marginal losses by a non-thermodependent
# component of intraspecific competition for species-1
# (scenario RCP2.6).
#######################################################################
lambda3 <- 0.002
lambda2 <- 1/2*lambda3
lambda1 <- 1/2*lambda2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = c(lambda1,lambda2,lambda3),
K2 = rep(1200,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
RCP = 2.6,
time_start = 2005,
time_end = 2100,
leap = 1/50)
#'#######################################################################
#Example 4: Different marginal losses by a non-thermodependent
# component of intraspecific competition for species-1
# (scenario RCP8.5).
#######################################################################
lambda3 <- 0.002
lambda2 <- 1/2*lambda3
lambda1 <- 1/2*lambda2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = c(lambda1,lambda2,lambda3),
K2 = rep(1200,3),
alpha = rep(0.02,3),
beta = rep(0.3,3),
RCP = 8.5,
time_start = 2005,
time_end = 2100,
leap = 1/50)
#######################################################################
#Example 5: Different competition coefficients (scenario RCP2.6).
#######################################################################
alpha1 <- 0.02
alpha2 <- 2*alpha1
alpha3 <- 2*alpha2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = rep(0.0005,3),
K2 = rep(1200,3),
alpha = c(alpha1,alpha2,alpha3),
beta = rep(0.3,3),
RCP = 2.6,
time_start = 2005,
time_end = 2100,
leap = 1/50)
#######################################################################
#Example 6: Different competition coefficients (scenario RCP8.5).
#######################################################################
alpha1 <- 0.02
alpha2 <- 2*alpha1
alpha3 <- 2*alpha2
competition(y_ini = c(N1 = 400, N1 = 400, N1 = 400,
N2 = 200, N2 = 200, N2 = 200),
temp_ini = rep(25,3),
temp_cmin = rep(20,3),
temp_cmax = rep(30,3),
ro = rep(0.5,3),
r2 = rep(0.4,3),
lambda1 = rep(0.0005,3),
K2 = rep(1200,3),
alpha = c(alpha1,alpha2,alpha3),
beta = rep(0.3,3),
RCP = 8.5,
time_start = 2005,
time_end = 2100,
leap = 1/50)
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