w_clim: Temperature trend obtained from WorldClim.
In Victor-Saldana/epcc: Simulating Populations of Ectotherms under Global Warming
w_clim R Documentation
Temperature trend obtained from WorldClim.
Description
This function allows simulating the effect of temperature trends on the
abundance of ectotherm populations in different geographic locations.
Temperature data is obtained from WorldClim.
Usage
w_clim(
y_ini = c(N = 400, N = 400, N = 400),
temp_cmin = rep(18, 3),
temp_cmax = c(25, 28, 32),
ro = rep(0.7, 3),
lambda = rep(5e-05, 3),
lat = rep(-33, 3),
lon = rep(-71, 3),
s = 10,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12
)
Arguments
y_ini
Initial population values (must be written with its name: N).
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.
lat, lon
Geographical coordinates of some place of interest of study, in decimal degrees.
s
Bioclimatic variable.
res
Spatial resolution.
time_start
Start of time sequence.
time_end
End of time sequence.
leap
Sequence increase.
Details
Three populations and/or scenarios can be simulated simultaneously. The temperature trends
are obtained by data extracted from WorldClim for the years 2000, 2050 and 2070 at a specific
location. The function internally calls the function getData of the raster package (Hijmans, 2020) to
obtain the bioclimatic variable of interest a given spatial resolution. An exponential
expression is fitted using the nls function. 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). In the first three examples, three geographic locations are
considered for Macrolophus pygmaeus as reported in Sánchez et al. (2012).
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
Hijmans, R.J. (2020). Package ‘raster’ (Version 3.3-13). pp. 1-249.
Rezende, E. L., & Bozinovic, F. (2019). Thermal performance across levels of biological
organization. Philosophical Transactions of the Royal Society B: Biological Sciences,
374(1778), 20180549.doi:10.1098/rstb.2018.0549
Sanchez, J. A., Spina, M. L., & Perera, O. P. (2012). Analysis of the population
structure of Macrolophus pygmaeus (Rambur) (Hemiptera: Miridae) in the Palaearctic
region using microsatellite markers. Ecology and Evolution, 2(12), 3145-3159.
doi:10.1002/ece3.420
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
## Not run:
#######################################################################
#Example 1: Different initial population abundances.
#######################################################################
w_clim(y_ini = c(N = 100, N = 200, N = 400),
temp_cmin = rep(18,3),
temp_cmax = rep(30,3),
ro = rep(0.7,3),
lambda = rep(0.00005,3),
lat = rep(-33,3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
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
w_clim(y_ini = c(N = 100, N = 100, N = 100),
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),
lat = rep(-33,3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Example 3: Different latitudes.
#######################################################################
lat1 <- -10
lat2 <- -33
lat3 <- -42
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(18,3),
temp_cmax = rep(40,3),
ro = rep(0.7,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
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
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(18,3),
temp_cmax = rep(30,3),
ro = rep(0.7,3),
lambda = c(lambda1,lambda2,lambda3),
lat = rep(-33,3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Application example I: Bioclimatic variable
# (Annual Mean Temperature).
#######################################################################
#We consider a population of Macrolophus pygmaeus in three different
#locations, and its intrinsic growth rate is adjusted to data obtained
#from Rezende and Bozinovic (2019).
github_link <- "https://github.com/Victor-Saldana/epcc/raw/main/M_pygmaeus.xlsx"
library(httr)
temp_file <- tempfile(fileext = ".xlsx")
req <- GET(github_link,
authenticate(Sys.getenv("GITHUB_PAT"), ""),
write_disk(path = temp_file))
M_pygmaeus <- readxl::read_excel(temp_file)
TPC <- rate_adjustment(data = M_pygmaeus)
#locality 1
lat1 <- 38.1827778
lon1 <- -1.7380555
#locality 2
lat2 <- 41.01384
lon2 <- 28.94966
#locality 3
lat3 <- 39.7213889
lon3 <- 21.63416638888889
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(TPC$temp_cmin,3),
temp_cmax = rep(TPC$temp_cmax,3),
ro = rep(TPC$ro,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = c(lon1,lon2,lon3),
s = 1,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Application example II: Bioclimatic variable
# (Max Temperature of Warmest Month).
#######################################################################
#We consider a population of Macrolophus pygmaeus in three different
#locations, and its intrinsic growth rate is adjusted to data obtained
#from Rezende and Bozinovic (2019).
github_link <- "https://github.com/Victor-Saldana/epcc/raw/main/M_pygmaeus.xlsx"
library(httr)
temp_file <- tempfile(fileext = ".xlsx")
req <- GET(github_link,
authenticate(Sys.getenv("GITHUB_PAT"), ""),
write_disk(path = temp_file))
M_pygmaeus <- readxl::read_excel(temp_file)
TPC <- rate_adjustment(data = M_pygmaeus)
#locality 1
lat1 <- 38.1827778
lon1 <- -1.7380555
#locality 2
lat2 <- 41.01384
lon2 <- 28.94966
#locality 3
lat3 <- 39.7213889
lon3 <- 21.63416638888889
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(TPC$temp_cmin,3),
temp_cmax = rep(TPC$temp_cmax,3),
ro = rep(TPC$ro,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = c(lon1,lon2,lon3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Application example III: Bioclimatic variable
# (Mean Temperature of Warmest Quarter).
#######################################################################
#We consider a population of Macrolophus pygmaeus in three different
#locations, and its intrinsic growth rate is adjusted to data obtained
#from Rezende and Bozinovic (2019).
github_link <- "https://github.com/Victor-Saldana/epcc/raw/main/M_pygmaeus.xlsx"
library(httr)
temp_file <- tempfile(fileext = ".xlsx")
req <- GET(github_link,
authenticate(Sys.getenv("GITHUB_PAT"), ""),
write_disk(path = temp_file))
M_pygmaeus <- readxl::read_excel(temp_file)
TPC <- rate_adjustment(data = M_pygmaeus)
#locality 1
lat1 <- 38.1827778
lon1 <- -1.7380555
#locality 2
lat2 <- 41.01384
lon2 <- 28.94966
#locality 3
lat3 <- 39.7213889
lon3 <- 21.63416638888889
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(TPC$temp_cmin,3),
temp_cmax = rep(TPC$temp_cmax,3),
ro = rep(TPC$ro,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = c(lon1,lon2,lon3),
s = 10,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
## End(Not run)
Victor-Saldana/epcc documentation built on Oct. 18, 2023, 6:57 a.m.
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| w_clim | R Documentation |
Temperature trend obtained from WorldClim.
Description
This function allows simulating the effect of temperature trends on the abundance of ectotherm populations in different geographic locations. Temperature data is obtained from WorldClim.
Usage
w_clim(
y_ini = c(N = 400, N = 400, N = 400),
temp_cmin = rep(18, 3),
temp_cmax = c(25, 28, 32),
ro = rep(0.7, 3),
lambda = rep(5e-05, 3),
lat = rep(-33, 3),
lon = rep(-71, 3),
s = 10,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12
)
Arguments
y_ini |
Initial population values (must be written with its name: N). |
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. |
lat, lon |
Geographical coordinates of some place of interest of study, in decimal degrees. |
s |
Bioclimatic variable. |
res |
Spatial resolution. |
time_start |
Start of time sequence. |
time_end |
End of time sequence. |
leap |
Sequence increase. |
Details
Three populations and/or scenarios can be simulated simultaneously. The temperature trends are obtained by data extracted from WorldClim for the years 2000, 2050 and 2070 at a specific location. The function internally calls the function getData of the raster package (Hijmans, 2020) to obtain the bioclimatic variable of interest a given spatial resolution. An exponential expression is fitted using the nls function. 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). In the first three examples, three geographic locations are considered for Macrolophus pygmaeus as reported in Sánchez et al. (2012).
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
Hijmans, R.J. (2020). Package ‘raster’ (Version 3.3-13). pp. 1-249.
Rezende, E. L., & Bozinovic, F. (2019). Thermal performance across levels of biological organization. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1778), 20180549.doi:10.1098/rstb.2018.0549
Sanchez, J. A., Spina, M. L., & Perera, O. P. (2012). Analysis of the population structure of Macrolophus pygmaeus (Rambur) (Hemiptera: Miridae) in the Palaearctic region using microsatellite markers. Ecology and Evolution, 2(12), 3145-3159. doi:10.1002/ece3.420
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
## Not run:
#######################################################################
#Example 1: Different initial population abundances.
#######################################################################
w_clim(y_ini = c(N = 100, N = 200, N = 400),
temp_cmin = rep(18,3),
temp_cmax = rep(30,3),
ro = rep(0.7,3),
lambda = rep(0.00005,3),
lat = rep(-33,3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
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
w_clim(y_ini = c(N = 100, N = 100, N = 100),
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),
lat = rep(-33,3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Example 3: Different latitudes.
#######################################################################
lat1 <- -10
lat2 <- -33
lat3 <- -42
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(18,3),
temp_cmax = rep(40,3),
ro = rep(0.7,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
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
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(18,3),
temp_cmax = rep(30,3),
ro = rep(0.7,3),
lambda = c(lambda1,lambda2,lambda3),
lat = rep(-33,3),
lon = rep(-71,3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Application example I: Bioclimatic variable
# (Annual Mean Temperature).
#######################################################################
#We consider a population of Macrolophus pygmaeus in three different
#locations, and its intrinsic growth rate is adjusted to data obtained
#from Rezende and Bozinovic (2019).
github_link <- "https://github.com/Victor-Saldana/epcc/raw/main/M_pygmaeus.xlsx"
library(httr)
temp_file <- tempfile(fileext = ".xlsx")
req <- GET(github_link,
authenticate(Sys.getenv("GITHUB_PAT"), ""),
write_disk(path = temp_file))
M_pygmaeus <- readxl::read_excel(temp_file)
TPC <- rate_adjustment(data = M_pygmaeus)
#locality 1
lat1 <- 38.1827778
lon1 <- -1.7380555
#locality 2
lat2 <- 41.01384
lon2 <- 28.94966
#locality 3
lat3 <- 39.7213889
lon3 <- 21.63416638888889
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(TPC$temp_cmin,3),
temp_cmax = rep(TPC$temp_cmax,3),
ro = rep(TPC$ro,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = c(lon1,lon2,lon3),
s = 1,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Application example II: Bioclimatic variable
# (Max Temperature of Warmest Month).
#######################################################################
#We consider a population of Macrolophus pygmaeus in three different
#locations, and its intrinsic growth rate is adjusted to data obtained
#from Rezende and Bozinovic (2019).
github_link <- "https://github.com/Victor-Saldana/epcc/raw/main/M_pygmaeus.xlsx"
library(httr)
temp_file <- tempfile(fileext = ".xlsx")
req <- GET(github_link,
authenticate(Sys.getenv("GITHUB_PAT"), ""),
write_disk(path = temp_file))
M_pygmaeus <- readxl::read_excel(temp_file)
TPC <- rate_adjustment(data = M_pygmaeus)
#locality 1
lat1 <- 38.1827778
lon1 <- -1.7380555
#locality 2
lat2 <- 41.01384
lon2 <- 28.94966
#locality 3
lat3 <- 39.7213889
lon3 <- 21.63416638888889
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(TPC$temp_cmin,3),
temp_cmax = rep(TPC$temp_cmax,3),
ro = rep(TPC$ro,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = c(lon1,lon2,lon3),
s = 5,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
#######################################################################
#Application example III: Bioclimatic variable
# (Mean Temperature of Warmest Quarter).
#######################################################################
#We consider a population of Macrolophus pygmaeus in three different
#locations, and its intrinsic growth rate is adjusted to data obtained
#from Rezende and Bozinovic (2019).
github_link <- "https://github.com/Victor-Saldana/epcc/raw/main/M_pygmaeus.xlsx"
library(httr)
temp_file <- tempfile(fileext = ".xlsx")
req <- GET(github_link,
authenticate(Sys.getenv("GITHUB_PAT"), ""),
write_disk(path = temp_file))
M_pygmaeus <- readxl::read_excel(temp_file)
TPC <- rate_adjustment(data = M_pygmaeus)
#locality 1
lat1 <- 38.1827778
lon1 <- -1.7380555
#locality 2
lat2 <- 41.01384
lon2 <- 28.94966
#locality 3
lat3 <- 39.7213889
lon3 <- 21.63416638888889
w_clim(y_ini = c(N = 100, N = 100, N = 100),
temp_cmin = rep(TPC$temp_cmin,3),
temp_cmax = rep(TPC$temp_cmax,3),
ro = rep(TPC$ro,3),
lambda = rep(0.00005,3),
lat = c(lat1,lat2,lat3),
lon = c(lon1,lon2,lon3),
s = 10,
res = 5,
time_start = 2000,
time_end = 2070,
leap = 1/12)
## End(Not run)
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