Overview
momo is a new R package for tagging-based movement modeling, specifically designed to estimate fine-scale animal movement patterns using various tagging data, such as mark-recapture, mark-resight, and data-logging/archival tags. The movement model is based on the advection-diffusion equation and incorporates habitat preference functions, enabling the reconstruction of individual movement paths and inference of population-level movement dynamics using a small set of interpretable parameters. Movement rates are estimated as a function of environmental conditions or geographic locations.
The current version of the package allows to estimate habitat preference functions and movement patterns. Further package development will allow to estimate spatiotemporally varying recapture probability, estimate natural and fishing mortality rates, and relative biomass indices and distribution.
To get started with momo, install and load the package, simulate a tagging dataset, and fit the movement model:
## devtools::install_github("tokami/momo/momo")
library(momo)
## Simulate a small tagging data set
sim <- sim.momo()
## Fit momo to the simulated data
fit <- fit.momo(sim)
## Plot simulated data and model predictions
plotmomo.compare(sim = sim, fit = fit)More information
More detailed examples and documentation for momo can be found at https://tokami.github.io/momo/. The pkgdown page includes links to articles, vignettes, functions descriptions, information to version updates, and much more. In case, your question is not answered by the package documentation and on the pkgdown pages, please write an email to the maintainer: Tobias Mildenberger. In case you find bugs, please post an issue on here.
