embryogrowth (version 6.4)

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 = -2, K = 0, K1 = 1, K2 = 0), males.freq = TRUE, fixed.parameters = NULL, SE = NULL, equation = "logistic", replicates = 1000, range.CI = 0.95, limit.low.TRT.minimum = 5, limit.high.TRT.maximum = 90, print = TRUE, temperatures.plot = seq(from = 20, to = 40, by = 0.1), durations.plot = seq(from = 15, to = 100, by = 0.1))

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

If TRUE data are shown as males frequency

fixed.parameters

Parameters that will not be changed

SE

Standard errors for parameters

equation

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

replicates

Number of replicates to estimate confidence intervals

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

temperatures.plot

Sequences of temperatures that will be used for plotting. If NULL, does not estimate them

durations.plot

Sequences of durations that will be used for plotting. If NULL, does not estimate them

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.

See Also

Other Functions.for.temperature.dependent.sex.determination: DatabaseTSD, TSP.list, predict.tsd, stages, tsd_MHmcmc_p, tsd_MHmcmc

Examples

## Not run: 
# CC_AtlanticSW <- subset(DatabaseTSD, RMU=="Atlantic, SW" & 
#                           Species=="Caretta caretta" & Sexed!=0)
# 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"))
# tsdDR <- with (CC_AtlanticSW, tsd(males=Males, females=Females, 
#                                  temperatures=Incubation.temperature-Correction.factor, 
#                                  equation="Double-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, 
#                DoubleRichards_model=tsdDR, GSD_model=gsd)
# ##############
# eo <- subset(DatabaseTSD, Species=="Emys orbicularis", c("Males", "Females", 
#                                        "Incubation.temperature"))
#                                        
# eo_Hill <- with(eo, tsd(males=Males, females=Females, 
#                                        temperatures=Incubation.temperature,
#                                        equation="Hill"))
# eo_Hill <- tsd(df=eo, 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"))
# 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)
# ### It can be used also for incubation duration
# CC_AtlanticSW <- subset(DatabaseTSD, RMU=="Atlantic, SW" & 
#                           Species=="Caretta caretta" & Sexed!=0)
# tsdL_IP <- with (CC_AtlanticSW, tsd(males=Males, females=Females, 
#                                  durations=IP.mean, 
#                                  equation="logistic"))
# plot(tsdL_IP, xlab="Incubation durations in days")
# ## End(Not run)