... a „Keep It Simple Species Migration model“ for R


What is KISSMig?

KISSMig is a simple, grid-based model for modeling the dynamics of species distributions in R. Starting from an initial distribution, KISSMig generates deterministic or stochastic species distributions for later points in time under constant or changing environmental conditions based on suitability maps. The name refers to the KISS principle and stands for a simple and fast executable model for the dynamic simulation of species distributions.

What's the use of it?

A central goal of macroecology and biogeography is a better understanding of species distributions. However, dynamic aspects such as the delayed migration of species are often neglected in predictions and simulations of the past, present or future distribution of species. Models that explicitly take such dynamics into account are usually complex and computationally intensive. KISSMig offers a simple way to integrate dynamic aspects such as limited migration into the modeling of species distributions (Nobis & Normand 2014).

How to start?

KISSMig is available directly in R as a package or can be installed via the download link below. KISSMig does not require prior knowledge of a species-specific migration rate, as this can be optimized depending on the species and structure of the analysis (Nobis & Normand 2014). The only input data are the initial geographic distribution, one or more suitability maps based on environmental data and the number of simulated migration steps. However, KISSMig can also be used to determine the probable past distribution of a species - for example, areas with glacial refugia (Nobis & Normand 2014). As an add-on module for statistical species distribution models (SDMs), KISSMig can be used to analyze limited migration of species under future climate change (Subba et al. 2018, Liao et al. 2020). The R script kissmig_examples.R can serve as a first introduction to the use of KISSMig in R.





The following downloads provide paleoclimate data with a temporal resolution of 1000 years since the last glacial maximum (LGM; -21'000 years) until today as used in Nobis & Normand (2014). After unpacking the zip files, the mean annual temperature and mean annual precipitation can be read into R using the stack function of the raster package.

In the meantime, CHELSA-TraCE21k (Karger et al. 2023) provides much more comprehensive, global paleoclimate data at higher resolution for the analysis of species distributions since the last glacial maximum.