Authors: Ludovica Luisa Vissat, Jason K. Blackburn, Wayne M. Getz
l.luisavissat@berkeley.edu, jkblackburn@ufl.edu, wgetz@berkeley.edu
All our material is stored in a Zenodo repository that can be found here. Note that the description and the names of the various files are provided in brackets on this webpage.
To generate the data used in Section 3, we defined and simulated the following NMB models, explained in detail in our SOF.
The distance-dependent behaviour model (Model_distance.nmb) can be downloaded here.
The time-dependent behaviour model (Model_time.nmb) can be downloaded here.
Visit numerusinc.com to download the latest version of NMB.
The output of a simulation of the distance-dependent behaviour model can be downloaded here for both individual A (ModelDistance_A.csv) and B (ModelDistance_B.csv).
The output of a simulation of the time-dependent behaviour model can be downloaded here for both individual A (ModelTime_A.csv) and B (ModelTime_B.csv).
The R code for the individual analysis, explained in Section 2, can be dowloaded here (AB_individual.R). This code has been used to generate Figure 5.
The R code for the dyadic analysis, explained in Section 2 and in our SOF, can be dowloaded here (AB_pair.R). This code has been used to generate Figure 6.
The R code used to plot the pair distance, colored according to the dyadic behaviour, can be downloaded here (Col_distance_AB.R). This code has been used to generate Figures 7 and 8.
We used the integrated development environment RStudio to perform all the analysis.
The data frames for each pair of interest can be downloaded here (DataFrames.zip). Each data frame is named “DataFramex_y_z.RData”, where x and y indicate the individual ID (see Tables 7-9 in SOF) and z the data frequency. These data frames contain the pair distance, time stamps1, locations, absolute and relative headings and their difference for each individual. These values were calculated for each time point which was at the appropriate chosen frequency with respect to its consecutive time point. These data frames are the result of the calculations shown in Algorithm 1 in the main paper, before the classification step.
In addition, we provide the data frame of the female-male pair of interest (DataFrame_Pair.RData), which contains also the calculated absolute heading difference and speed for both individuals.
The R code used for the creation of these data frames for the analysis can be dowloaded here (DataFrame.R).
The R code for the individual analysis can be dowloaded here both for the barplots (Ind_behaviour_barplot.R) and for the statistical analysis (Ind_behaviour_stat_analysis.R). This code has been used to generate Figure 9.
The R code for the dyadic analysis can be dowloaded here (Pair_analysis.R). This code has been used to generate Figure 10.
The R code for the extended analysis, explained in Section 2 and in our SOF, can be dowloaded here (Extended_analysis.R). This code has been used to generate Figure 12.
1 Note that the dates are in the numeric format. Please use the following R code to transform them in a date format, if needed: as.POSIXct(datenumeric, origin=”1970-01-01”, tz = “UTC”)