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("http://marc.girondot.free.fr/CRAN/HelpersMG.tar.gz", repos=NULL, type="source")
install.packages("http://marc.girondot.free.fr/CRAN/embryogrowth.tar.gz", repos=NULL, type="source")
Details
Fit a parametric function that describes dependency of embryo growth to temperature
Package: | embryogrowth |
Type: | Package |
Version: | 10.2 build 2094 |
Date: | 2025-06-16 |
License: | GPL (>= 2) |
LazyLoad: | yes |
Author(s)
Marc Girondot marc.girondot@gmail.com
References
Girondot M (1999).
“Statistical description of temperature-dependent sex determination using maximum likelihood.”
Evolutionary Ecology Research, 1(3), 479-486.
Godfrey MH, Delmas V, Girondot M (2003).
“Assessment of patterns of temperature-dependent sex determination using maximum likelihood model selection.”
Ecoscience, 10(3), 265-272.
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.
doi:10.1016/j.jtherbio.2014.08.005.
Girondot M, Kaska Y (2015).
“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.
doi:10.1016/j.jtherbio.2014.10.008.
Fuentes MM, Monsinjon J, Lopez M, Lara P, Santos A, dei Marcovaldi MA, Girondot M (2017).
“Sex ratio estimates for species with temperature-dependent sex determination differ according to the proxy used.”
Ecological Modelling, 365, 55-67.
doi:10.1016/j.ecolmodel.2017.09.022.
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(2), 172-179.
doi:10.2744/CCB-1269.1.
Girondot M, Godfrey MH, Guillon J, Sifuentes-Romero I (2018).
“Understanding and integrating resolution, accuracy and sampling rates of temperature data loggers used in biological and ecological studies.”
Engineering Technology Open Access Journal, 2(4), 55591.
Girondot M, Monsinjon J, Guillon J (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.
doi:10.1016/j.jtherbio.2018.02.006.
Abreu-Grobois FA, Morales-Mérida BA, Hart CE, Guillon J, Godfrey MH, Navarro E, Girondot M (2020).
“Recent advances on the estimation of the thermal reaction norm for sex ratios.”
PeerJ, 8, e8451.
doi:10.7717/peerj.8451, https://peerj.com/articles/8451/.
Morales Mérida A, Helier A, Cortés-Gómes AA, Girondot M (2021).
“Hatching success rather than temperature-dependent sex determination as the main driver of Olive Ridley (Lepidochelys olivacea) nest density in the Pacific Coast of Central America.”
Animals (Basel), 11, 3168.
doi:10.3390/ani11113168.
Monsinjon J, Guillon J, Wyneken J, Girondot M (2022).
“Thermal reaction norm for sexualization: the missing link between temperature and sex ratio for temperature-dependent sex determination.”
Ecological Modelling, 473(110119), 1-7.
doi:10.1016/j.ecolmodel.2022.110119.
Morales-Mérida BA, Morales-Cabrera A, Chúa C, Girondot M (2023).
“Olive ridley sea turtle incubation in natural conditions is possible on Guatemalan beaches.”
Sustainability, 15, 14196.
doi:10.3390/su151914196.
Hulin V, Delmas V, Girondot M, Godfrey MH, Guillon J (2009).
“Temperature-dependent sex determination and global change: Are some species at greater risk?”
Oecologia, 160(3), 493-506.
Tello-Sahagún LA, Ley-Quiñonez CP, Abreu-Grobois FA, Monsinjon JR, Zavala-Norzagaray AA, Girondot M, Hart CE (2023).
“Neglecting low season nest protection exacerbates female biased sea turtle hatchling production through the loss of male producing nests.”
Biological Conservation, 277, 109873.
doi:10.1016/j.biocon.2022.109873.
Morales-Mérida BA, Contreras-Mérida MR, Girondot M (2019).
“Pipping dynamics in marine turtle Lepidochelys olivacea nests.”
Trends in Developmental Biology, 12, 23-30.
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$data, bty="n", las=1,
xlab="Days of incubation", ylab="Temperatures in °C",
series="all",
type="l", xlim=c(0,70),ylim=c(20, 35))
par(mar=c(4, 4, 1, 1))
pMCMC <- TRN_MHmcmc_p(resultNest_4p_SSM, accept=TRUE)
# Take care, it can be very long, sometimes several days
resultNest_mcmc_4p_SSM <- GRTRN_MHmcmc(result=resultNest_4p_SSM,
parametersMCMC=pMCMC, n.iter=10000, n.chains = 1, n.adapt = 0,
thin=1, trace=TRUE)
data(resultNest_mcmc_4p_SSM)
out <- as.mcmc(resultNest_mcmc_4p_SSM)
# This out obtained after as.mcmc can be used with coda package
# plot() can use the direct output of GRTRN_MHmcmc() function.
plot(resultNest_mcmc_4p_SSM, parameters=1, xlim=c(0,550))
plot(resultNest_mcmc_4p_SSM, parameters=3, xlim=c(290,320))
# But rather than to use the SD for each parameter independantly, it is
# more logical to estimate the distribution of the curves
new_result <- ChangeSSM(resultmcmc = resultNest_mcmc_4p_SSM, result = resultNest_4p_SSM,
temperatures = seq(from = 20, to = 35, by = 0.1),
initial.parameters = NULL)
par(mar=c(4, 4, 1, 5)+0.4)
plotR(result = resultNest_4p_SSM, parameters = new_result$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,
ylim=c(0, 8),
curve = "MCMC quantiles", show.density=TRUE)
plotR(resultNest_6p_SSM, resultmcmc=resultNest_mcmc_6p_SSM,
ylim=c(0, 8), 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)