dispfit: An R package to estimate species dispersal kernels

Dispersal of organisms is a ubiquitous aspect of the natural world, with wide implications across scales and organization levels. Interest in dispersal has risen sharply over the past 30 years, mostly due to the multiple and rapid global changes ecosystems face. Among the various aspects that may characterize a dispersion event, dispersal distance is considered a key descriptor in a wide variety of studies across taxonomic groups. Typically, dispersal distances are defined in the form of dispersal kernels describing the dispersal distance distribution according to probability density functions. Although numerous methods providing dispersal data exist, there is still a lack of intuitive and comprehensive approaches and tools to estimate dispersal kernels from such data. Here we present the dispfit package, an R software application developed to fill this gap. dispfit fits and compares different families of parameterized functions to describe and predict dispersal distances. It includes 9 well-known and commonly used distributions, computes goodness-of-fit and model selection statistics, and estimate each distribution's parameters, along with their first four moments (mean, standard deviation, skewness, and kurto-sis). We describe the main functions included in dispfit and provide an example to illustrate the workflow of the typical analyses performed within the package. We believe that dispfit will critically contribute to improving the modelling of species' dispersal distances, thus enhancing the understanding of the ecological and evolutionary processes involving dispersal movement.

Automated summary

Organism dispersal is a widespread phenomenon with significant implications across various scales and levels of organization. The dispfit package, introduced in this article, is an R software application that provides intuitive and comprehensive tools to estimate and describe dispersal distances. It includes 9 commonly used distributions, computes goodness-of-fit and model selection statistics, and estimates distribution parameters and moments. We believe that dispfit will greatly contribute to improving the modeling of species' dispersal distances and enhancing our understanding of ecological and evolutionary processes involving dispersal movement.