This package allows for the estimation and simulation of home range behaviour, including both landscape patch dynamics (step selection) and memory effects. The mechanistic movement model allows you to: “(1) quantify the role of memory in the movements of a large mammal reintroduced into a novel environment, and (2) predict observed patterns of home range emergence in this experimental setting” Ranc et al. (2022).
How to cite this package
Ranc, N., Cagnacci, F., Moorcroft, P.R., 2022. Memory drives the formation of animal home ranges: Evidence from a reintroduction. Ecology Letters 25, 716–728. https://doi.org/10.1111/ele.13869.
Installation
stable release (PENDING)
To install the current stable release use a CRAN repository:
install.packages("homeranger")
library("homeranger")development release
To install the development releases of the package run the following commands:
if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/homeranger")
library("homeranger")Vignettes are not rendered by default, if you want to include additional documentation please use:
if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/homeranger", build_vignettes = TRUE)
library("homeranger")Use
The current release is the first refactored version of the original C++ framework into R (with a C++ backend). The data handling is therefore not optimized. However, a reference example is given below, recreating the original results in Ranc et al. (2022).
library(homeranger)
library(terra)
# read in the reference data, these are calculated with the
# shared original code and provide the step based likelihoods
# this output should match the output of the package for parity
reference <- read_csv("data-raw/validation/objective_function_detail.csv")
reference$likelihood[reference$likelihood == -9999] <- NA
# specify the parameters as used in the default run
# this is would be config_best_Mmem_fitting.txt
params <- list(
r_l = 27.5332236990522,
w_l = 0,
r_d = 0.018257876686841,
w_d = 0.9999,
r_dist = 0.0412536435305482,
w_dist = 0.9999,
step_length_dist = 0.00216275705935606,
step_length_shape = 1.14267311221975,
threshold_approx_kernel = 7000,
threshold_memory_kernel = 1000,
# resource selection coefficients should be
# a named list for driver data layer validation
# and correct data processing
coef = c(
"slope" = 0.272835968106296,
"slope_sq" = -0.093687792157105,
"tcd_325grain"= 0.177991482087775,
"tcd_325grain_sq" = -0.140639949444926,
"landcover_5322" = 0.591063382485486,
"landcover_agri" = -0.811974081226742
)
)
# sort and subset data
# load the raster data in a data cube, and reorder the layers
# based upon the order of the coefficients in the parameter
# list - finally convert to 3D array to be passed to the
# low level C++ functions
r <- terra::rast(list.files("data-raw/drivers/","*.asc", full.names = TRUE))
r <- as.array(subset(r, names(params$coef)))
r[is.na(r)] <- 0
# run the model for these parameters
# in optimization mode (to check a traceable output)
# there should be ~parity as this is deterministic
output <- hr_predict(
data = r,
par = params,
obs = "data-raw/tracks/Aspromonte_roedeer_traj.txt",
resolution = 25,
optimization = TRUE,
verbose = TRUE
)
# plot the 1:1 graph - should be spot on
output$likelihood[output$likelihood == -9999] <- NA
plot(output$likelihood, reference$likelihood)
abline(0,1)Acknowledgements
The original model development was supported by the Harvard University Graduate Fellowship and a Fondazione Edmund Mach International Doctoral Programme Fellowship, with data and code made available at the Zenodo Digital Repository (https://doi.org/10.5281/zenodo.5189835 and https://doi.org/10.5281/zenodo.5208215, repository). The logo background was created by Mohamed Smina and freely shared through Vecteezy.com. Refactoring and further model development was supported by the French Office for Biodiversity on the TRANSLOC project (OF-25-0032).