embryogrowth-package {embryogrowth}R Documentation

The package embryogrowth

Description

Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
The latest version of this package can always been installed using:
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/HelpersMG.tar.gz", repos=NULL, type="source")
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/embryogrowth.tar.gz", repos=NULL, type="source")
embryogrowth logo

Details

Fit a parametric function that describes dependency of embryo growth to temperature

Package: embryogrowth
Type: Package
Version: 9.1 build 1630
Date: 2023-10-24
License: GPL (>= 2)
LazyLoad: yes

Author(s)

Marc Girondot marc.girondot@gmail.com

References

Girondot, M. & Kaska, Y. 2014. A model to predict the thermal reaction norm for the embryo growth rate from field data. Journal of Thermal Biology. 45, 96-102.

Fuentes, M.M.P.B., Monsinjon, J., Lopez, M., Lara, P., Santos, A., dei Marcovaldi, M.A.G., Girondot, M., 2017. Sex ratio estimates for species with temperature-dependent sex determination differ according to the proxy used. Ecological Modelling 365, 55-67.

Girondot, M., Monsinjon, J., Guillon, J.-M., 2018. Delimitation of the embryonic thermosensitive period for sex determination using an embryo growth model reveals a potential bias for sex ratio prediction in turtles. Journal of Thermal Biology 73, 32-40.

Monsinjon, J., Jribi, I., Hamza, A., Ouerghi, A., Kaska, Y., Girondot, M., 2017. Embryonic growth rate thermal reaction norm of Mediterranean Caretta caretta embryos from two different thermal habitats, Turkey and Libya. Chelonian Conservation and Biology 16, 172-179.

See Also

Delmas, V., Prevot-Julliard, A.-C., Pieau, C. & Girondot, M. 2008. A mechanistic model of temperature-dependent sex determination in a Chelonian, the European pond turtle. Functional Ecology, 22, 84-93.

Girondot, M., Ben Hassine, S., Sellos, C., Godfrey, M. & Guillon, J.-M. 2010. Modeling thermal influence on animal growth and sex determination in Reptiles: being closer of the target gives new views. Sexual Development, 4, 29-38.

Girondot, M. 1999. Statistical description of temperature-dependent sex determination using maximum likelihood. Evolutionary Ecology Research, 1, 479-486.

Girondot, M., & Kaska, Y. (2014). Nest temperatures in a loggerhead- nesting beach in Turkey is more determined by sea surface temperature than air temperature. Journal of Thermal Biology, 47, 13-18.

Examples

## Not run: 
library("embryogrowth")
packageVersion("embryogrowth")
data(nest)
formated <- FormatNests(nest)
# The initial parameters value can be:
# "T12H", "DHA",  "DHH", "Rho25"
# Or
# "T12L", "DT", "DHA",  "DHH", "DHL", "Rho25"
x <- structure(c(115.758929130522, 428.649022170996, 503.687251738993, 
12.2621455821612, 306.308841227278, 116.35048615105), .Names = c("DHA", 
"DHH", "DHL", "DT", "T12L", "Rho25"))
# or
x <- structure(c(118.431040984352, 498.205702157603, 306.056280989839, 
118.189669472381), .Names = c("DHA", "DHH", "T12H", "Rho25"))
# pfixed <- c(K=82.33) or rK=82.33/39.33
pfixed <- c(rK=2.093313)

################################################################################
#
# The values of rK=2.093313 and M0=1.7 were used in 
# Girondot, M. & Kaska, Y. 2014. A model to predict the thermal 
# reaction norm for the embryo growth rate from field data. Journal of
# Thermal Biology. 45, 96-102.
#
# Based on recent analysis on table of development for both Emys orbicularis and 
# Caretta caretta, best value for rK should be 1.209 and M0 should be 0.34.
# Girondot M, Monsinjon J, Guillon J-M (2018) Delimitation of the embryonic 
# thermosensitive period for sex determination using an embryo growth model 
# reveals a potential bias for sex ratio prediction in turtles. Journal of 
# Thermal Biology 73: 32-40 
#
# See the example in the stages datasets
# 
################################################################################

resultNest_4p_SSM <- searchR(parameters=x, fixed.parameters=pfixed, 
	temperatures=formated, integral=integral.Gompertz, M0=1.7, 
	hatchling.metric=c(Mean=39.33, SD=1.92))
data(resultNest_4p_SSM)
par(mar=c(4, 4, 1, 1))
plot(resultNest_4p_SSM$data[[1]][, 1]/60/24,resultNest_4p_SSM$data[[1]][, 2], bty="n", las=1, 
     xlab="Days of incubation", ylab="Temperatures in °C", 
     type="l", xlim=c(0,70),ylim=c(20, 35))
for (i in 2:resultNest_4p_SSM$data$IndiceT[["NbTS"]]) {
  par(new=TRUE)
  plot(resultNest_4p_SSM$data[[i]][, 1]/60/24,resultNest_4p_SSM$data[[i]][, 2], 
  bty="n", las=1, xlab="", ylab="", type="l", xlim=c(0,70),ylim=c(20, 35), axes = FALSE)
}
par(mar=c(4, 4, 1, 1))
pMCMC <- TRN_MHmcmc_p(resultNest_4p_SSM, accept=TRUE)
# Take care, it can be very long, sometimes several days
resultNest_mcmc_4p_SSM <- GRTRN_MHmcmc(result=resultNest_4p_SSM,  
	parametersMCMC=pMCMC, n.iter=10000, n.chains = 1, n.adapt = 0,  
	thin=1, trace=TRUE)
data(resultNest_mcmc_4p_SSM)
out <- as.mcmc(resultNest_mcmc_4p_SSM)
# This out obtained after as.mcmc can be used with coda package
# plot() can use the direct output of GRTRN_MHmcmc() function.
plot(resultNest_mcmc_4p_SSM, parameters=1, xlim=c(0,550))
plot(resultNest_mcmc_4p_SSM, parameters=3, xlim=c(290,320))
# But rather than to use the SD for each parameter independantly, it is
# more logical to estimate the distribution of the curves
new_result <- ChangeSSM(resultmcmc = resultNest_mcmc_4p_SSM, result = resultNest_4p_SSM,
                        temperatures = seq(from = 20, to = 35, by = 0.1), 
                        initial.parameters = NULL)
par(mar=c(4, 4, 1, 5)+0.4)

plotR(result = resultNest_4p_SSM, parameters = new_result$par, 
           ylabH = "Temperatures\ndensity", ylimH=c(0, 0.3), atH=c(0, 0.1, 0.2), 
           ylim=c(0, 3), show.hist=TRUE)
      
# Beautiful density plots

plotR(result = resultNest_4p_SSM, 
             resultmcmc=resultNest_mcmc_4p_SSM, 
             curve = "MCMC quantiles", show.density=TRUE)

plotR(resultNest_6p_SSM, resultmcmc=resultNest_mcmc_6p_SSM, 
            ylim=c(0, 4), show.density=TRUE, show.hist=TRUE, 
            curve = "MCMC quantiles", 
            ylimH=c(0,0.5), atH=c(0, 0.1, 0.2))

# How many times this package has been download
library(cranlogs)
embryogrowth <- cran_downloads("embryogrowth", from = "2014-08-16", 
                            to = Sys.Date() - 1) 
sum(embryogrowth$count)
plot(embryogrowth$date, embryogrowth$count, type="l", bty="n")

## End(Not run)
[Package embryogrowth version 9.1 Index]