embryogrowth
Tools to Analyze the Thermal Reaction Norm of Embryo Growth

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stages: Database of of embryonic development and thermosensitive...

stages: Database of of embryonic development and thermosensitive...
In embryogrowth: Tools to Analyze the Thermal Reaction Norm of Embryo Growth

stagesR Documentation

Database of of embryonic development and thermosensitive period of development for sex determination

Description

Database of embryonic development and thermosensitive period of development for sex determination.

Usage

stages

Format

A list with dataframes including attributes

Details

Database of embryonic development and thermosensitive period of development for sex determination

Author(s)

Marc Girondot marc.girondot@universite-paris-saclay.fr

References

Pieau, C., Dorizzi, M., 1981. Determination of temperature sensitive stages for sexual differentiation of the gonads in embryos of the turtle, Emys orbicularis. Journal of Morphology 170, 373-382.

Yntema, C.L., Mrosovsky, N., 1982. Critical periods and pivotal temperatures for sexual differentiation in loggerhead sea turtles. Canadian Journal of Zoology-Revue Canadienne de Zoologie 60, 1012-1016.

Kaska, Y., Downie, R., 1999. Embryological development of sea turtles (Chelonia mydas, Caretta caretta) in the Mediterranean. Zoology in the Middle East 19, 55-69.

Greenbaum, E., 2002. A standardized series of embryonic stages for the emydid turtle Trachemys scripta. Canadian Journal of Zoology-Revue Canadienne de Zoologie 80, 1350-1370.

Magalhães, M.S., Vogt, R.C., Sebben, A., Dias, L.C., de Oliveira, M.F., de Moura, C.E.B., 2017. Embryonic development of the Giant South American River Turtle, Podocnemis expansa (Testudines: Podocnemididae). Zoomorphology.

See Also

Other Functions for temperature-dependent sex determination: DatabaseTSD, DatabaseTSD.version(), P_TRT(), ROSIE, ROSIE.version(), TSP.list, plot.tsd(), predict.tsd(), tsd(), tsd_MHmcmc(), tsd_MHmcmc_p()

Examples

## Not run: 
library(embryogrowth)
data(stages)
names(stages)
levels(as.factor(stages$Species))
# Version of database
stages$Version[1]
kaska99.SCL <- subset(stages, subset=(Species == "Caretta caretta"), 
         select=c("Stage", "SCL_Mean_mm", "SCL_SD_mm", "Days_Begin", "Days_End"))

kaska99.SCL[kaska99.SCL$Stage==31, "Days_Begin"] <- 51
kaska99.SCL[kaska99.SCL$Stage==31, "Days_End"] <- 62
kaska99.SCL <- na.omit(kaska99.SCL)
kaska99.SCL[which(kaska99.SCL$Stage==31), "Stage"] <- c("31a", "31b", "31c")
kaska99.SCL <- cbind(kaska99.SCL, 
                     Days_Mean=(kaska99.SCL[, "Days_Begin"]+kaska99.SCL[, "Days_End"])/2)
kaska99.SCL <- cbind(kaska99.SCL, 
                     Days_SD=(kaska99.SCL[, "Days_End"]-kaska99.SCL[, "Days_Begin"])/4)
Gompertz <- function(x, par) {
   K <- par["K"]
   rT <- par["rT"]
   X0 <- par["X0"]
   y <- abs(K)*exp(log(abs(X0)/abs(K))*exp(-rT*x))
   return(y)
 }

ML.Gompertz <- function(x, par) {
  par <- abs(par)
  y <- Gompertz(x, par)
  return(sum(-dnorm(y, mean=kaska99.SCL[, "SCL_Mean_mm"], 
                    sd=kaska99.SCL[, "SCL_SD_mm"], log=TRUE)))
}

parIni <- structure(c(48.66977358, 0.06178453, 0.38640902), 
                   .Names = c("K", "rT", "X0"))

fitsize.SCL <- optim(parIni, ML.Gompertz, x=kaska99.SCL[, "Days_Mean"], hessian = TRUE)

# Estimation of standard error of parameters using Hessian matrix
sqrt(diag(solve(fitsize.SCL$hessian)))

# Estimation of standard error of parameters using Bayesian  concept and MCMC
pMCMC <- structure(list(Density = c("dunif", "dunif", "dunif"), 
                        Prior1 = c(0, 0, 0), Prior2 = c(90, 1, 2), 
                        SDProp = c(1, 1, 1), 
                        Min = c(0, 0, 0), Max = c(90, 1, 2), 
                        Init = fitsize.SCL$par), 
                   .Names = c("Density", "Prior1", "Prior2", "SDProp", "Min", "Max", "Init"), 
                   row.names = c("K", "rT", "X0"), class = "data.frame")

Bayes.Gompertz <- function(data, x) {
  x <- abs(x)
  y <- Gompertz(data, x)
  return(sum(-dnorm(y, mean=kaska99.SCL[, "SCL_Mean_mm"], 
                    sd=kaska99.SCL[, "SCL_SD_mm"], log=TRUE)))
}

mcmc_run <- MHalgoGen(n.iter=50000, parameters=pMCMC, data=kaska99.SCL[, "Days_Mean"], 
                     likelihood=Bayes.Gompertz, n.chains=1, n.adapt=100, thin=1, trace=1, 
                      adaptive = TRUE)

plot(mcmc_run, xlim=c(0, 90), parameters="K")
plot(mcmc_run, xlim=c(0, 1), parameters="rT")
plot(mcmc_run, xlim=c(0, 2), parameters="X0")

1-rejectionRate(as.mcmc(mcmc_run))

par <- mcmc_run$resultMCMC[[1]]

outsp <- t(apply(par, MARGIN = 1, FUN=function(x) Gompertz(0:70, par=x)))

rangqtiles <- apply(outsp, MARGIN=2, function(x) {quantile(x, probs=c(0.025, 0.5, 0.975))})

par(mar=c(4, 4, 2, 1))
plot_errbar(x=kaska99.SCL[, "Days_Mean"], y=kaska99.SCL[, "SCL_Mean_mm"], 
            errbar.y = 2*kaska99.SCL[, "SCL_SD_mm"], bty="n", las=1, 
            ylim=c(0, 50), xlab="Days", ylab="SCL mm", 
           xlim=c(0, 70), x.plus = kaska99.SCL[, "Days_End"], 
            x.minus = kaska99.SCL[, "Days_Begin"])

lines(0:70, rangqtiles["2.5%", ], lty=2)
lines(0:70, rangqtiles["97.5%", ], lty=2)
lines(0:70, rangqtiles["50%", ], lty=3)

text(x=50, y=10, pos=4, labels=paste("K=", format(x = fitsize.SCL$par["K"], digits = 4)))
text(x=50, y=12.5, pos=4, 
   labels=paste("rK=", format(x = fitsize.SCL$par["K"]/39.33, digits = 4)))
text(x=50, y=15, pos=4, labels=paste("X0=", format(x = fitsize.SCL$par["X0"], digits = 4)))
title("Univariate normal distribution")

# Using a multivariate normal distribution

library(mvtnorm)

 ML.Gompertz.2D <- function(x, par) {
   par <- abs(par)
  y <- Gompertz(x, par)
  L <- 0
  for (i in seq_along(y)) {
    sigma <- matrix(c(kaska99.SCL$SCL_SD_mm[i]^2, 0, 0, kaska99.SCL$Days_SD[i]^2), 
                    nrow=2, byrow=TRUE, 
                    dimnames=list(c("SCL_SD_mm", "Days_SD"), c("SCL_SD_mm", "Days_SD")))
    L <- L -dmvnorm(x=c(SCL_SD_mm=kaska99.SCL$SCL_Mean_mm[i], 
                    Days_SD=kaska99.SCL$Days_Mean[i]), 
                    mean= c(SCL_SD_mm=y[i], Days_SD=kaska99.SCL$Days_Mean[i]), 
                            sigma=sigma, log=TRUE)
  }
  return(L)
}

parIni <- structure(c(48.66977358, 0.06178453, 0.38640902), 
                    .Names = c("K", "rT", "X0"))

fitsize.SCL.2D <- optim(parIni, ML.Gompertz.2D, x=kaska99.SCL[, "Days_Mean"], hessian = TRUE)

# Estimation of standard error of parameters using Hessian matrix
sqrt(diag(solve(fitsize.SCL.2D$hessian)))

# Estimation of standard error of parameters using Bayesian  concept and MCMC
Bayes.Gompertz.2D <- function(data, x) {
  x <- abs(x)
  y <- Gompertz(data, x)
  L <- 0
  for (i in seq_along(y)) {
    sigma <- matrix(c(kaska99.SCL$SCL_SD_mm[i]^2, 0, 0, kaska99.SCL$Days_SD[i]^2), 
                    nrow=2, byrow=TRUE, 
                    dimnames=list(c("SCL_SD_mm", "Days_SD"), c("SCL_SD_mm", "Days_SD")))
    L <- L - dmvnorm(x=c(SCL_SD_mm=kaska99.SCL$SCL_Mean_mm[i], 
                         Days_SD=kaska99.SCL$Days_Mean[i]), 
                    mean= c(SCL_SD_mm=y[i], Days_SD=kaska99.SCL$Days_Mean[i]), 
                    sigma=sigma, log=TRUE)
  }
  return(L)
}

pMCMC <- structure(list(Density = c("dunif", "dunif", "dunif"), 
                        Prior1 = c(0, 0, 0), Prior2 = c(90, 1, 2), 
                        SDProp = c(1, 1, 1), 
                        Min = c(0, 0, 0), Max = c(90, 1, 2), 
                        Init = fitsize.SCL.2D$par), 
                   .Names = c("Density", "Prior1", "Prior2", "SDProp", "Min", "Max", "Init"), 
                   row.names = c("K", "rT", "X0"), class = "data.frame")
mcmc_run.2D <- MHalgoGen(n.iter=50000, parameters=pMCMC, data=kaska99.SCL[, "Days_Mean"], 
                     likelihood=Bayes.Gompertz.2D, n.chains=1, n.adapt=100, thin=1, trace=1, 
                      adaptive = TRUE)

plot(mcmc_run.2D, xlim=c(0, 90), parameters="K")
plot(mcmc_run.2D, xlim=c(0, 1), parameters="rT")
plot(mcmc_run.2D, xlim=c(0, 2), parameters="X0")

1-rejectionRate(as.mcmc(mcmc_run.2D))

par <- mcmc_run.2D$resultMCMC[[1]]

outsp <- t(apply(par, MARGIN = 1, FUN=function(x) Gompertz(0:70, par=x)))

rangqtiles <- apply(outsp, MARGIN=2, function(x) {quantile(x, probs=c(0.025, 0.5, 0.975))})

par(mar=c(4, 4, 2, 1))
plot_errbar(x=kaska99.SCL[, "Days_Mean"], y=kaska99.SCL[, "SCL_Mean_mm"], 
            errbar.y = 2*kaska99.SCL[, "SCL_SD_mm"], bty="n", las=1, 
            ylim=c(0, 50), xlab="Days", ylab="SCL mm", 
           xlim=c(0, 70), x.plus = kaska99.SCL[, "Days_End"], 
            x.minus = kaska99.SCL[, "Days_Begin"])

lines(0:70, rangqtiles["2.5%", ], lty=2)
lines(0:70, rangqtiles["97.5%", ], lty=2)
lines(0:70, rangqtiles["50%", ], lty=3)

text(x=50, y=10, pos=4, 
     labels=paste("K=", format(x = fitsize.SCL.2D$par["K"], digits = 4)))
text(x=50, y=12.5, pos=4, 
     labels=paste("rK=", format(x = fitsize.SCL.2D$par["K"]/39.33, digits = 4)))
text(x=50, y=15, pos=4, 
     labels=paste("X0=", format(x = fitsize.SCL.2D$par["X0"], digits = 4)))
title("Multivariate normal distribution")


## End(Not run)

embryogrowth documentation built on Sept. 11, 2024, 8:16 p.m.

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