embryogrowth (version 5.1)

tsd: Estimate the parameters that best describe temperature-dependent sex determination

Description

Estimate the parameters that best describe temperature-dependent sex determination

Usage

tsd(df = NULL, males = NULL, females = NULL, N = NULL,
  temperatures = NULL, durations = NULL, l = 0.05,
  parameters.initial = c(P = NA, S = -0.5, K = 0, K1 = 1, K2 = 0),
  males.freq = TRUE, las.x = 1, las.y = 1,
  lab.PT = "Pivotal temperature",
  lab.TRT = paste0("Transitional range of temperatures l=", l * 100, "%"),
  col.TRT = "gray", col.TRT.CI = rgb(0.8, 0.8, 0.8, 0.5),
  col.PT.CI = rgb(0.8, 0.8, 0.8, 0.5), show.CI = TRUE,
  equation = "logistic", replicate = 1000, range.CI = 0.95,
  limit.low.TRT.minimum = 5, limit.high.TRT.maximum = 90, print = TRUE,
  show.plot = TRUE, ...)

Arguments

df

A dataframe with at least two columns named males, females or N and temperatures, Incubation.temperature or durations column

males

A vector with male numbers

females

A vector with female numbers

N

A vector with total numbers

temperatures

The constant incubation temperatures used to fit sex ratio

durations

The duration of incubation or TSP used to fit sex ratio

l

The limit to define TRT (see Girondot, 1999)

parameters.initial

Initial values for P, S or K search as a vector, ex. c(P=29, S=-0.3)

males.freq

Should the graph uses males frequency [TRUE] or females [FALSE]

las.x

las parameter for x axis

las.y

las parameter for y axis

lab.PT

Label to describe pivotal temperature

lab.TRT

Label to describe transitional range of temperature

col.TRT

The color of TRT

col.TRT.CI

The color of CI of TRT based on range.CI

col.PT.CI

The color of CI of PT based on range.CI

show.CI

Do the CI for the curve should be shown

equation

Could be "logistic", "Hill", "Richards", "Hulin", "Double-Richards" or "GSD"

replicate

Number of replicate to estimate SE of TRT

range.CI

The range of confidence interval for estimation, default=0.95

limit.low.TRT.minimum

Minimum lower limit for TRT

limit.high.TRT.maximum

Maximum higher limit for TRT

print

Do the results must be printed at screen? TRUE (default) or FALSE

show.plot

Do the plot must be shown? TRUE (default) or FALSE

...

Graphical parameters for plot(), exemple xlab="", ylab="", main=""

Value

• A list the pivotal temperature, transitional range of temperatures and their SE

Details

tsd estimates the parameters that best describe temperature-dependent sex determination

References

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

Godfrey, M.H., Delmas, V., Girondot, M., 2003. Assessment of patterns of temperature-dependent sex determination using maximum likelihood model selection. Ecoscience 10, 265-272.

Hulin, V., Delmas, V., Girondot, M., Godfrey, M.H., Guillon, J.-M., 2009. Temperature-dependent sex determination and global change: are some species at greater risk? Oecologia 160, 493-506.

Girondot M., Submited. On the concept of embryological thermosensitive period for sex determination in reptiles.

Examples

CC_AtlanticSW <- subset(STSRE_TSD, RMU=="Atlantic, SW" &
                          Species=="Caretta caretta" & Sexed!=0)
par(mar=c(4,4,5,1)+0.1)
tsdL <- with (CC_AtlanticSW, tsd(males=Males, females=Females,
                                 temperatures=Incubation.temperature-Correction.factor,
                                 equation="logistic"))
tsdH <- with (CC_AtlanticSW, tsd(males=Males, females=Females,
                                 temperatures=Incubation.temperature-Correction.factor,
                                 equation="Hill"))
tsdR <- with (CC_AtlanticSW, tsd(males=Males, females=Females,
                                 temperatures=Incubation.temperature-Correction.factor,
                                 equation="Richards"))
gsd <- with (CC_AtlanticSW, tsd(males=Males, females=Females,
                                 temperatures=Incubation.temperature-Correction.factor,
                                 equation="GSD"))
compare_AIC(Logistic_Model=tsdL, Hill_model=tsdH, Richards_model=tsdR,
               GSD_model=gsd)
##############
eo <- subset(STSRE_TSD, Species=="Emys orbicularis", c("Males", "Females",
                                       "Incubation.temperature"))

eo_Hill <- with(eo, tsd(males=Males, females=Females,
                                       temperatures=Incubation.temperature,
                                       equation="Hill"))
eo_logistic <- tsd(eo)
eo_Richards <- with(eo, tsd(males=Males, females=Females,
                                 temperatures=Incubation.temperature,
                                 equation="Richards"))
### The Hulin model is a modification of Richards (See Hulin et al. 2009)
### limit.low.TRT and limit.high.TRT must be setup for Hulin equation
par <- eo_Richards$par
names(par)[which(names(par)=="K")] <- "K2"
par <- c(par, K1=0)
eo_Hulin <- with(eo, tsd(males=Males, females=Females,
                                 parameters.initial=par,
                                 temperatures=Incubation.temperature,
                                 equation="Hulin",
                                 limit.low.TRT.minimum=25,
                                 limit.high.TRT.maximum=35))
### The Double-Richards model is a Richards model with K1 and K2 using the two values
### below and above P
par <- eo_Richards$par
names(par)[which(names(par)=="K")] <- "K2"
par <- c(par, K1=as.numeric(par["K2"])-0.1)
par["K1"] <- par["K1"]-0.1
eo_Double_Richards <- with(eo, tsd(males=Males, females=Females,
                                 parameters.initial=par,
                                 temperatures=Incubation.temperature,
                                 equation="Double-Richards"))
### Example with only points and mean curve
eo_Double_Richards <- with(eo, tsd(males=Males, females=Females,
                                 parameters.initial=par,
                                 temperatures=Incubation.temperature,
                                 equation="Double-Richards",
                                 col.TRT="white", col.TRT.CI="white", col.PT.CI="white"))
compare_AIC(Logistic=eo_logistic, Hill=eo_Hill, Richards=eo_Richards,
             Hulin=eo_Hulin, Double_Richards=eo_Double_Richards)
### Note the asymmetry of the Double-Richards model
predict(eo_Double_Richards,
       temperatures=c(eo_Double_Richards$par["P"]-0.2, eo_Double_Richards$par["P"]+0.2))
predict(eo_Double_Richards)