devRate (version 0.1.1)

devRateIBM: Forecast ectotherm phenology as a function of temperature and developmental rate models

Description

Forecast ectotherm phenology as a function of temperature and developmental rate models

Usage

devRateIBM(tempTS, timeStepTS, models, numInd = 100, stocha, timeLayEggs = 1)

Arguments

tempTS

The temperature time series (a vector).

timeStepTS

The time step of the temperature time series (a numeric).

models

The models for developmental rate (a list with objects of class nls).

numInd

The number of individuals for the simulation (an integer).

stocha

The standard deviation of a Normal distribution centered on develomental rate to create stochasticity among individuals (a numeric).

timeLayEggs

The delay between emergence of adults and the time where females lay eggs in time steps (a numeric).

Value

A list with three elements: the table of phenology for each individual, the models used (nls objects), and the time series for temperature.

Examples

rawDevEggs <- matrix(c(10, 0.031, 10, 0.039, 15, 0.047, 15, 0.059, 15.5, 0.066,
   13, 0.072, 16, 0.083, 16, 0.100, 17, 0.100, 20, 0.100, 20, 0.143, 25, 0.171,
   25, 0.200, 30, 0.200, 30, 0.180, 35, 0.001), ncol = 2, byrow = TRUE)
rawDevLarva <- matrix(c(10, 0.010, 10, 0.014, 10, 0.019, 13, 0.034, 15, 0.024,
   15.5, 0.029, 15.5, 0.034, 15.5, 0.039, 17, 0.067, 20, 0.050, 25, 0.076,
   25, 0.056, 30, 0.0003, 35, 0.0002), ncol = 2, byrow = TRUE)
rawDevPupa <- matrix(c(10, 0.001, 10, 0.008, 10, 0.012, 13, 0.044, 15, 0.017,
   15, 0.044, 15.5, 0.039, 16, 0.034, 15.5, 0.037, 16, 0.051, 17, 0.051,
   20, 0.080, 20, 0.092, 25, 0.102, 25, 0.073, 30, 0.005,
   35, 0.0002), ncol = 2, byrow = TRUE)
mEggs <- devRateModel(eq = taylor_81, temp = rawDevEggs[,1], devRate = rawDevEggs[,2],
   startValues = list(Rm = 0.05, Tm = 30, To = 5))
mLarva <- devRateModel(eq = taylor_81, temp = rawDevLarva[,1], devRate = rawDevLarva[,2],
   startValues = list(Rm = 0.05, Tm = 25, To = 5))
mPupa <- devRateModel(eq = taylor_81, temp = rawDevPupa[,1], devRate = rawDevPupa[,2],
   startValues = list(Rm = 0.05, Tm = 30, To = 5))
forecastTsolanivora <- devRateIBM(
   tempTS = rnorm(n = 100, mean = 15, sd = 1),
   timeStepTS = 1,
   models = list(mEggs, mLarva, mPupa),
   numInd = 500,
   stocha = 0.015,
   timeLayEggs = 1)