aof {aof} | R Documentation |

Ontogenetic shifts in central-place foraging insects

aof(name, Age, x)

`name` |
The identity of the insect (e.g. a bee) as factor (e.g. "A00103C00020C301", "bee1"). |

`Age` |
The age of the insect in day as numeric (e.g. 1, 4, 32). |

`x` |
The daily activity of the insect at a given age as a numeric value, for instance (i) the number of the trips per day, (ii) the duration of the trips per day, or (iii) the time of the trips per day. |

A breakpoint-based method to detect ontogenetic shifts in univariate time-activity budget series of central-place foraging insects. The method finds a single change point according to the likelihood function. The method was developed with honey bees in order to detect the Age at Onset of Foraging (AOF), but can be used for the detection of other ontogenetic shifts in other central-place foraging insects. For more details, see Requier et al. (2020) Measuring ontogenetic shifts in central-place foraging insects: a case study with honey bees. Journal of Animal Ecology.

A data.frame containing the aof results (one row per insect).

require("bcpa") # Exemple with simulated data: # A study case with with change simulated (i.e. mu1 and mu2 are different) # -------------------------------------------------- mu1 <- 25 # behavioural value at stage 1 mu2 <- 50 rho1 <- 0.5 # interval frequency at stage 1 rho2 <- rho1 # Low number of individuals (N, n.obs) and low variance (V, sigma) # create time series from 0 to 50 with a behavioural change at 25 t.full <- 0:50 t.break <- 25 n.obs <- 35 # no. observations randomly selected in the time series: 5 to 45 sigma1 <- 0.1 # variance: 0.1 to 3 sigma2 <- sigma1 SimTS <- function(n, mu, rho, sigma){ X.standard <- arima.sim(n, model = list(ar = rho)) X.standard/sd(X.standard)*sigma + mu } x.full <- c(SimTS(t.break, mu1, rho1, sigma1), SimTS(max(t.full)-t.break+1, mu2, rho2, sigma2)) # subsample of observations (n defined above) and estimate keep <- sort(sample(1:length(x.full), n.obs)) TimeBudget <- data.frame( name = "A", Age = t.full[keep], x = x.full[keep]) # Running the algorithm AOF <- aof( name = TimeBudget$name, Age = TimeBudget$Age, x = TimeBudget$x) print(AOF) # Exemple with real data extracted from Requier et al. # (2020, J. Animal Ecology): # -------------------------------------------------- TimeBudget <- dataExample # working on Number of trips varX <- "Number" AOF_number <- aof( name = TimeBudget[,1], Age = TimeBudget[,2], x = TimeBudget[varX]) print(AOF_number) # working on Duration of trips varX <- "Duration" AOF_duration <- aof( name = TimeBudget[,1], Age = TimeBudget[,2], x = TimeBudget[varX]) print(AOF_duration) # working on Time of trips varX <- "Time" AOF_time <- aof( name = TimeBudget[,1], Age = TimeBudget[,2], x = TimeBudget[varX]) print(AOF_time) # see vignette for more examples

[Package *aof* version 0.1.2 Index]